Invited Talks
Talk 1: Artificial Intelligence for Industry, Environment, and Ambient Intelligence
Pr. Vincenzo PIURI
FIEEE, Department of computer Science, Università degli Studi di Milano, Italy.
Pr. Vincenzo PIURI has received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He is Full Professor in computer engineering at the Università degli Studi di Milano, Italy (since 2000). He has been Associate Professor at Politecnico di Milano, Italy and Visiting Professor at the University of Texas at Austin and at George Mason University, USA. His main research interests are: intelligent systems, artificial intelligence, signal and image processing, machine learning, pattern analysis and recognition, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, fault tolerance, dependability, and cloud computing infrastructures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters. He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He has been IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-19), and Associate Editor of the IEEE Transactions on Computers and the IEEE Transactions on Cloud Computing, and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement. He received the IEEE Instrumentation and Measurement Society Technical Award (2002). He is Honorary Professor at Obuda University, Budapest, Hungary, Guangdong University of Petrochemical Technology, China, Muroran Institute of Technology, Japan, and the Amity University, India.
Abstract
Adaptability and advanced services for industrial manufacturing require an intelligent technological support for understanding the production process characteristics also in complex situations. Quality control is specifically one of the activities in manufacturing which is very critical for ensuring high-quality products and competitiveness on the market. Similarly, protection of the environment requires ability to adjust the understanding of the current status by considering the natural dynamics of the environment itself and the natural phenomena. Finally, creating adaptive environments for the better life of people requires an appropriate support for understanding the current needs and the desires of users in the interactions with the environment for their daily use, as well as for understanding the current status of the environment also in complex situations. This infrastructure constitutes an essential base for smart living. Artificial intelligence can provide additional flexible techniques for designing and implementing monitoring and control systems both for industrial and environmental applications as well as for ambient intelligence, which can be configured from behavioral examples or by mimicking approximate reasoning processes to achieve adaptable systems. This talk will analyze the opportunities offered by artificial intelligence technologies to support the realization of adaptable operations and intelligent services in industrial applications, specifically focusing on manufacturing processes and quality control, in environmental monitoring, especially for land management and agriculture, and in ambient intelligence, in particular for smart environments.
Talk 2: Agent based approach: Distributed problem solving and complex system modeling
Pr. Abderrafiaa KOUKAM
Université de Technologie de Belfort Montbéliard, 90010, Belfort Cedex France
http://www.multiagent.fr/People:Koukam_abderrafiaa
https://scholar.google.com/citations?user=r-8ibQwAAAAJ&hl=fr
Pr. Dr. Abderrafiaa Koukam is a professor at the "Université de Technologie de Belfort-Montbéliard" (UTBM). He obtained his Ph.D. at the "Université de Nancy 1" and his Habilitation to direct research at the "Université de Bourgogne". He was Director of the "Laboratoire Systèmes et Transports", Director of Computer Science Department and the founder of the team Multi-agent Systems and Optimization. His research focuses on multi-agent systems: modeling, simulation, and verification. He assumed the coordination of two European projects (TRASCOM, SURE 2003-2005), several national projects (CRISTAL, ANR SafePlatoon,...), and contracts with industry in the following areas: transportation planning, mobile networks, traffic flow simulation, and intelligent vehicle.
Currently, he is Vice President of the Scientific Council of UTBM and member of the French National University Committee.
Abstract
Multi-agent approach proposes a new vision of problem analysis and system modeling, based on a radical critique of centralized methods. It considers systems as societies of autonomous and independent entities, called agents, which interact to solve a problem or collectively perform a task.
Multi-agent systems are used in several application domains, in particular, robotics, distributed problem solving, modeling and simulation of complex systems.
The purpose of this presentation is to introduce the foundation of this approach by considering the two perspectives: distributed problem solving and simulation of complex systems.
Talk 3: Take the Best of Big Data: Focus on Value & Variety
Pr. Ladjel BELLATRECHE
ISAE-ENSMA, Poitiers University, Futuroscope, Poitiers, France
Pr. Ladjel BELLATRECHE is a full Professor at National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA), Poitiers, France, where he joined as a faculty member since Sept. 2010. He leads the Data and Model Engineering Team of the Laboratory of Computer Science and Automatic Control for Systems (LIAS). Prior to that, he spent eight years as Assistant and then Associate Professor at Poitiers University, France. He was a Visiting Professor of the Québec en Outaouais, Canada, a Visiting Researcher at the Department of Computer Science, Purdue University, USA and the Department of Computer Science of Hong Kong University of Science and Technology, China (1997-1999). His research interest focuses on Data and Model Management. He has co-authored more than 250 papers and received > 2516 citations (H-index=26). He serves as an Associate Editor of the Data & Knowledge (DKE) Journal, Elsevier, an Editorial Board of International Journal of Reasoning-based Intelligent Systems, Inderscience, Scalable Computing Journal, Springer and Computer Science and Information Systems Journal. He organized/co-organized numerous international and French Conferences and Workshops (ADBIS, DAWAK, ACM DOLAP, MEDI, IEEE SCC, IEEE Smart Data, WISE, EDA, BDA). He has acted as evaluator for funding agencies in Algeria, France, EU, Czech Republic, Kazakhstan and Netherlands. He actively contributes in promoting research in Africa and Asia, where he co-supervised several students. He co-found several conferences and workshops (MEDI, CIIA, OAIS).
Abstract
Big data represents a new technology for managing data with high velocity, volume, variety and contributes to creating value for companies. Capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends. The Big Data Era has largely contributed in accelerating the development strategic plans issued from governments and research organisms, coving the management, exploitation and analysis of these data by taking into account the different V's of Big Data. Among these plans, we can cite for instance the development of: (a) large-scale platforms (ex. data-clusters, distributed data clusters), (b) Software Defined Environments (SDE) (ex. IBM SDE), (c) advanced programming paradigms (ex. map-reduce, Spark, etc.), Data Analytics Tools (Rapid Miner, Google Fusion Tables, Solver), (d) Visualization tools (Google Chart, Tableau, Oracle Visual Analyzer), and (h) high quality and valuable Knowledge Bases (KB), constructed either by academicians (e.g., Cyc, DBpedia, Freebase, and YAGO) and industrials (e.g., Google Knowledge Graph, Facebook Knowledge Graph, Amazon Knowledge Graph, Credit Rating Agencies, Enterprise Knowledge Base, etc.). In this talk, we would like to foster the creation of a think tank dedicated to getting the best from Big Data V’s and the efforts related to it to revisit our research activities without compromising them. In this talk, we would like to share the experience conducted with our Model and Data Engineering Team of the LIAS Laboratory at ISAE-ENSMA, which aims at the design of data warehousing applications. Based on the literature, this design is based on two main approaches: (i) a supply-driven approach (also called data-driven) that starts with an analysis of operational data sources in order to identify all the available data and (ii) a user-driven approach (also known as requirement-driven or goal-orientated) which stems from the determination of the information requirements of different business users. Several studies and experiments show that resorting to these two approaches entails a high risk for companies, since some functional requirements cannot be satisfied. This is due to the lack of relevant data in sources. In parallel, reference studies have identified the crucial role of knowledge bases (KB) for analytical tasks, by offering analysts more entities (people, places, products, etc.). The availability of a huge, high quality valuable KB is an asset for data warehousing designers and decision-makers to construct/exploit a valuable data warehouse. So, faced with this situation, we here present a value-driven approach that revisits the traditional life cycle of the design of data warehouses, by considering KB as an external resource. These different phases are illustrated via the YAGO KB.
