Invited Keynote Speakers

yannis_manolopoulosYannis Manolopoulos, Professor, Department of Informatics of the Aristotle University of Thessaloniki

Title: Skyline Queries: an introduction


A case of preference queries that have attracted significant interest are the skyline queries, which have been used in several multi-criteria decision support applications. Given a dominance relationship in a dataset, a skyline query returns the objects that cannot be dominated by any other object. Skyline queries have been studied extensively in multidimensional spaces, in subspaces, in high-dimensional spaces, in metric spaces, in dynamic spaces, in streaming environments, and in time-series data. Several algorithms have been proposed for skyline query processing: window-based, progressive, distributed, geometric-based, index-based, divide-and-conquer, and dynamic programming algorithms. Moreover, several variations of skyline queries have been proposed to solve application-specific problems like: k-dominant skylines, top-k dominating queries, spatial skyline queries, and others. As the number of objects that are returned in a skyline query may become large, there is also an extensive study for the cardinality of skyline queries. We have studied the cardinality of skyline and top-k dominating queries in multi-dimensional data, and we have proposed formulae for the estimation of their cardinality. This extensive research depicts the importance of skyline queries and their variations in modern applications.

Short Bio:

Yannis Manolopoulos is Professor with the Department of Informatics of the Aristotle University of Thessaloniki. He has been with the University of Toronto, the University of Maryland at College Park and the University of Cyprus. He has also served as Rector of the University of Western Macedonia in Greece, Head of his own department, and Vice-Chair of the Greek Computer Society. His research interest focuses in Data Management.

He has co-authored 5 monographs and 8 textbooks in Greek, as well as ~300 journal and conference papers. He has received >8500 citations from >1200 distinct academic institutions (h-index=42). He has also received 3 best paper awards from SIGMOD, ECML/PKDD and MEDES conferences and has been invited as keynote speaker in 10 international events. He has served as main co-organizer of several major conferences (among others): ADBIS 2002, SSTD 2003, SSDBM 2004, ICEIS 2006, EANN 2007, ICANN 2010, AIAI 2012, WISE 2013, CAISE 2014, MEDI 2015. He has also acted as evaluator for funding agencies in Austria, Canada, Cyprus, Czech Republic, Estonia, EU, Hong-Kong, Georgia, Greece, Israel, Italy and Russia. Currently, he serves in the Editorial Boards of (among others) The VLDB Journal, The World Wide Web Journal, The Computer Journal.

Andreas Spanias, Professor in Digital Signal Processing  (DSP),   Director SenSIP Center, Arizona State University, USA.

Title: Advances in Speech and Audio Processing and Coding


This plenary session will cover speech processing research advances with the emphasis on speech and audio coding methods.  In the session, we will discuss the fundamental principles, techniques, and algorithms used in current coding applications including a summary of codecs for telecommunication standards. The session will start with a discussion on: the basic speech representation methods, the performance measures used to evaluate coded speech, and the role of the standards. Brief algorithm descriptions include: ADPCM, sub-band coding, adaptive transform coding, sinusoidal transform coding (STC), linear predictive coding (LPC), and analysis-by-synthesis LPC (sparse excitation, code excited LPC, and ACELP). The presentation will feature audio, and computer demonstrations of recent speech coding standards including voice-over IP algorithms. The plenary session will also cover wideband audio standards such as MPEG audio and other layers (e.g., MP3, AAC).  Recent algorithms will also be described including the following:  Variable-Rate Multimode Wideband (VMR-WB), Speex,  G722.1, OGG Vorbis 2012, iLBC, SELT, SILK, Opus 2013, Qualcomm wideband 5G codecs.  At the end of the session, we will cover briefly recent applications that use voice features for detecting speech pathologies, and also discuss methods for obtaining long term speech parameters and using them as predictors of other deceases such as tremor, Alzheimers etc.

Short bio:

Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering  at Arizona State University (ASU). He is also the director of the Sensor Signal and Information Processing  (SenSIP) center and the founder of the SenSIP industry consortium (now an NSF I/UCRC site). His research interests are in the areas of adaptive signal processing, speech processing, and  sensor systems. He and his student team developed the computer simulation software Java-DSP and its award winning  iPhone/iPad and Android versions. He is author of two text books: Audio Processing and Coding by Wiley and DSP; An Interactive Approach (2nd Ed.). He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished lecturer for the IEEE Signal processing society in 2004. He is a series editor for the Morgan and Claypool lecture series on algorithms and software.

Sukarno J. Mertoguno
Office of Naval Research, USA

Title: Symbiotic Statistical and Formal Reasoning


The speech will provide a broad overview of research sponsored by ONR related to cyber security and complex software.  Emphasis will be given to new concepts for symbiotic integration of formal rule-based and statistical-based machine learning and reasoning.
Real-time autonomy is a key element for system which closes the loop between observation, interpretation, planning, and action, commonly found in UxV, robotics, smart vehicle technologies, automated industrial machineries, and ONR’s autonomic computing. Real-time autonomic cyber system requires timely and accurate decision making and adaptive planning. Autonomic decision making understands its own state and the perceived state of its environment. It is capable of anticipating changes and future states and projecting the effects of actions into future states. Understanding of current state and the knowledge/model of the world are needed for extrapolating actions and deriving action plans. Humans have gut-feeling (fast and shallow reasoning, and reflexive actions) and deliberative thinking (slower and deeper reasoning, and deliberate actions). Models of how these two systems of intelligence interact with each other have been proposed for human decision-making, e.g., the two-systems model by Daniel Kahneman.  These interacting models could inspire approaches for machine reasoning in autonomic cyber and cyber-physical systems. Methods are being developed for merging statistical inference and formal reasoning by merging their knowledge a unified representation. An alternative method ONR is promoting, took the direction where statistical inference and formal reasoning systems live side-by-side and interact, prompt, inform and correct each other. Integration of statistical inference and formal reasoning should be done in such a way to allow each of the methods to independently and semi-redundantly but synergistically cooperate and potentially enrich each other’s knowledge base (cross fertilization).

Short bio:

Dr. J. Sukarno Mertoguno manages basic and applied sience research in cyber security and complex software for The Office of Naval Research (ONR). Before joining ONR he worked as a system & chip architect and an entrepreneur in the Silicon Valley, where he has worked on various chips and systems, such as embedded processors, switching fabric, network processors, and various other hardware accelerators, including TCP/IP, NFS, mobile anti-malware, etc. He received a Ph.D. In electrical engineering from SUNY-Binghamton. He also has background in Theoretical Physics.


Stratis Kanarachos
Coventry University, UK

Title: Anomaly detection methods and applications in transportation


The last decade we have experienced a boom in sensor utilization and information collection. Sensors and data acquisition systems have become cheap and widely available. Smartphones equipped with sensors are being used almost everywhere. At the same the Internet of Things made possible to connect devices, machines and structures through a network and exchange information between them. Of particular significance is the timely and accurate exchange of safety critical information such as the detection of damage or of abnormal operating conditions. The applications are vast, just to mention a few: transportation, structural health monitoring, medicine, power distribution, economics. The normal behaviour of machines and structures is described by data that follow regular time patterns. Conversely, abnormal behaviour disturbs regularity and causes deviations from the regular time pattern. Most of the anomaly detection algorithms proposed up to now are based on specific experts’ knowledge of the domain of interest. What becomes slowly and increasingly important is the development of generic and widely applicable anomaly detection tools. In this presentation, we will provide an overview of applications and methods used for this purpose.

Short bio:

Prof. Stratis Kanarachos graduated in 2001 from the Mechanical Engineering Course, National Technical University of Athens (NTUA), Greece and he earned his Ph.D. degree from the same institution in 2004. He held the position of Lecturer and Assistant Professor at Frederick University (Cyprus) for the periods 2005-2007 and 2007-2012 respectively in the area of Vibrations & Dynamics. In the period 2012-2014 he worked at the Integrated Vehicle Safety Department of TNO, the Netherlands as a senior researcher. His main research activities involved the development of state estimation techniques as well as model reduction methods. Since 2014 he is a Senior Lecturer and Managing Director of the Jaguar Land Rover TAS Scheme at Coventry University. He is the author of more than 75 publications in peer reviewed International scientific journals and conferences. He has actively proposed and executed more than 25 research projects funded by the industry or the European Commission.