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Invited Speakers

[Nov. 6, 2017 – Morning]: Prof. George Tsihrintzis, University of Piraeus

[Nov. 7, 2017 – Morning]: Dr. Bruce Kramer, National Science Foundation

[Nov. 7, 2017 – Gala Dinner]: Prof. Nikolaos Bourbakis, Wright State University

[Nov. 8, 2017 – Morning]: Doctoral Presentation Session

KEYNOTE TALK 1: Classification with Significant Class Imbalance and Applications in Software Personalization

Professor George Thihrintzis, University of Piraeus, Greece


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.


George A. Tsihrintzis is Full Professor and Head of the Department of Informatics 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 14 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 guest co-edited 9 special issues of international journals. He was the recipient of 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 was the recipient of 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.” He was the recipient of one of the Best Student Paper Awards of the 5th IEEE International Conference on Information, Intelligence, Systems and Applications (IISA2014), Chania, Crete, Greece, July, 7-9, 2014, for co-authoring a paper titled: “Genetic-AIRS: A Hybrid Classification Method based on Genetic Algorithms and Artificial Immune Systems”

KEYNOTE TALK 2: Rethinking Manufacturing as a Search-Based Service

Dr. Bruce Kramer, Senior Advisor – National Science Foundation, USA


Many industries have been transformed by tools that give users easy access to service providers, but manufacturing services depend on specialized, expert-to-expert, designer-to-manufacturer negotiations. The presentation will attempt to reframe design as a process that can be driven by an intelligent search of the existing store of parts that have already been manufactured. Each of those parts has a detailed 3D geometrical description, an associated manufacturer and a proven process plan that can be depended on to execute reliably with small parametric variations. An effective search strategy would be a critical breakthrough that resolves longstanding and critical problems with the prevailing generative view of design and manufacturing, which depends on perfecting software systems that unambiguously translate the designs of expert designers into the associated machine instructions for producing them. It is suggested that the search-based approach provides an interesting challenge for the machine learning community and a few speculative approaches will be presented.


Dr. Bruce Kramer is a graduate of MIT (S.B., S.M., Ph.D) and has served on the faculties of Mechanical Engineering of MIT and George Washington University. He is currently a Senior Advisor at the NSF, coordinating NSF’s participation in the National Advanced Manufacturing Program. Dr. Kramer previously directed NSF’s Divisions of Design, Manufacture and Industrial Innovation and Engineering Education and Centers. He co-founded Zoom Telephonics of Boston, a NASDAQ company and producer of communications products marketed under the Zoom and Motorola brands, holds three U.S. patents, and is a Fellow of the SME and an International Fellow of the School of Engineering of the University of Tokyo. He has received the F.W. Taylor Medal of CIRP, the ASME Blackall Award, and the R.F. Bunshah Medal of the ICMC for his contributions to manufacturing research and the Distinguished Service Award, the highest honorary award granted by the National Science Foundation.

KEYNOTE TALK 3: The Evolution of Artificial Intelligence and its Impact to Humanity

Prof. Nikolaos Bourbakis, Wright State University, USA


The recent advances in Artificial Intelligence have triggered the curiosity of a small number of young scientist to change the way that humanity looks like. The idea of a human body that acts only as the host of the brain and does not perform any other action gains popularity among young AI researchers. In this talk I would like to address these challenges and associated issues and every one has the right to think about them.


Nikolaos G. Bourbakis received the Ph.D. degree in Computer Engineering and Informatics from the University of Patras, Patras, Greece. He is currently the Ohio Board Region Distinguished Professor of Information Technology in the Engineering College, a joint appointment Professor in the Geriatrics Dept. School of Medicine, the Director of the Center of Assistive Research Technologies (CART), Wright State University, Dayton OH, and a Visiting Research Professor in ECE Department at Ohio State University, Columbus OH. His research interests include applied artificial intelligence, machine vision, human machine interfaces, bioengineering, assistive technologies, and cyber-security funded by USA and European government and industry. He has published more than 420 articles in refereed international journals, book chapters and conference proceedings, and 10 books as an author, co-author or editor. He is the founder and the Editor-In Chief of the International Journal on AI Tools and EIC of the Int. Journal on Monitoring and Surveillance Research Technologies (IGI-Global Pub.), the Editor in Charge of a research series of books in AI (WS Publisher) 1994-2004, the founder and general chair of several international IEEE computer society conferences, symposia and workshops. He is or was an Associate Editor in several IEEE and international journals, and a guest editor in 20 special issues in IEEE and international journals related to his research interests. He is an IEEE Fellow, a Distinguished IEEE Computer Society Speaker, an NSF University Research Programs Evaluator, an IEEE Computer Society Golden Core Member, President of the Biological Artificial Intelligence Society, an External Evaluator in University Promotion Committees, an Official Nominator of the National Academy of Achievements for Computer Science Programs, and a keynote speaker in several international conferences. His research work has been internationally recognized and has won several prestigious awards. He is also listed in many organizations (Who’s Who in Engineering, in Science, in Education, in Intellectuals, in Computer Engineering, AMWS, List of Distinguished Editors, etc.) and is the recipient of several prestigious awards from IBM, IEEE, and IEEE CS for his research contributions.

SPECIAL EVENT: Doctoral Presentation Session


It is a great pleasure for the ICTAI-2017 Organizers to offer a free Doctoral Presentation Session the last day of the ICTAI-Conference. In particular, we invite all the PhD graduate ICTAI-student-authors (who have passed the qualifications exams and are “candidates for a PhD”) to prepare one PPT slice. This PPT slice will include the tentative title of the PhD degree, the student’s name, the Advisor’s name and a very short statement for the goals of the PhD research work. Each student-author will have 3 minutes to present his work to the Conference audience. No questions will be asked. Some benefits of this Doctoral Presentation Session are:

1. The students inform the participants about their research (visibility);

2. Some participants may interact with some student-authors for possible future collaboration or employment;

3. The student-authors become familiar with the ICTAI activities and may be future participants and organizers;