• The 11th International ACM Conference on Management of Digital EcoSystems (MEDES'19) - 12-14 November 2019, Limassol - Cyprus

  • Paper Submission Deadline: July 8, 2019

  • Author Notification: August 12, 2019

  • Venue: Miramare Hotel

Using Massive Vehicle Trajectory Data for Routing Visual Analytics Framework:
For enhanced decision making using cascaded AI technologies
Beyond Silicon Valley: can Universities act as innovation powerhouses in a period of disruption?

key3

key1

mar15

Christian S. Jensen
Dimitrios Tzovaras
Marios Dikaiakos

Using Massive Vehicle Trajectory Data for Routing

key3

by Christian S. Jensen

Departement of Computer Science - Aalborg University
Denmark

Abstract

As the ongoing society-wide digitalization unfolds, important societal processes are being captured at an unprecedented level of detail, in turn enabling us to better understand and improve those processes. Vehicular transportation is one such process, where populations of vehicles are able to generate massive volumes of trajectories that may in turn be used for offering better routing to the vehicles. In particular, with massive trajectory data available, the traditional routing paradigm, where a road network is modeled as an edge-weighted graph, is no longer adequate. Instead, new paradigms that thrive on massive trajectory data are called for. The talk will cover several such paradigms, including path-centric, on-the-fly, and cost-oblivious routing. Even massive volumes of trajectory data are sparse in these settings, which calls for a variety of means of making good use of the available data.

Biography

Christian S. Jensen is Obel Professor of Computer Science at Aalborg University, Denmark, and he was recently with Aarhus University for three years and spent a one-year sabbatical at Google Inc., Mountain View. His research concerns data management and data-intensive systems, and its focus is on temporal and spatio-temporal analytics. Christian is an ACM and an IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He has received several national and international awards for his research. He is Editor-in-Chief of ACM Transactions on Database Systems.

A Visual Analytics Framework for enhanced decision making using cascaded AI technologies

key1

by Dimitrios Tzovaras

Center for Research and Technology Hellas - Information Technologies Institute
Greece

Abstract

Taking advantage of the massive amounts of processing power offered by modern CPUs & GPUs, state-of-the-art AI is rapidly moving from traditional pattern recognition and machine learning technologies to deep neural architectures. Despite the impressive performance of the latter, they are still able to carry out only very specific operations, with limited configuration margin, esp. when their training has been completed. Their, yet undiscovered to its full extent, potential for unprecedented accuracy lies within the depth of their structure, i.e. the large number of layers, that achieves to encode paramount intelligence tailored to the exact needs of their objective; always upon correct and adequate training. Inspired by the effectiveness of such multi-layered approaches, the current session will introduce a framework implemented as a cascaded architecture of Deep Neural Networks (DNNs) that aims to implement Decision Making Support with efficient applicability in a wide range of fields of applications. Task-specific DNNs lie in the basis of the pyramid, while the DNNs found higher in the hierarchy act as "control gates" for the ones below, while seamlessly offering "dimensionality reduction", "data minimization" & "data mining" through fusion, classification & clustering techniques. Yet, of both extreme operational & scientific interest is the involvement of the human operator in the loop, so as to benefit from their expertise and to interactively serve their preferences & needs per case. In order to achieve this, the crown of the proposed framework consists of a Visual Analytics toolset that not only allows for comprehensive visualizations of the produced results/outcomes, but it also offers advanced interaction possibilities. This way, a direct link to the aforementioned control-gates is established, transmitting the appropriate operational configuration to the lower processing layers, forming thus, the desired communication loop between human intelligence & AI. The applicability of the proposed framework is demonstrated in a series of real-world use cases that involve a series of distinct challenging tasks but also the intensive feedback by each operator. In particular, they range from (i) the detection & mitigation of cybersecurity attacks in a real-world IoT networks and (ii) early event detection from social media & news feeds, to (iii) real-time defect detection on materials in the production line and (iv) DNA sequences analysis.

Biography

Dr. Dimitrios Tzovaras is a Senior Researcher Grade A’ (Professor) and the Director at CERTH/ITI (the Information Technologies Institute of the Centre for Research and Technology Hellas). He received the Diploma in Electrical Engineering and the Ph.D. in 2D and 3D Image Compression from the Aristotle University of Thessaloniki, Greece in 1992 and 1997, respectively. Prior to his current position, he was a Senior Researcher on the Information Processing Laboratory at the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki. His main research interests include visual analytics, computer vision, data fusion, machine learning and artificial intelligence, network and computer security, biometrics and virtual reality. He is author or co-author of over 130 articles in refereed journals and over 350 papers in international conferences. Since 2004, he has been Associate Editor in the following International journals: Journal of Applied Signal Processing (JASP) and Journal on Advances in Multimedia of EURASΙP. Additionally, he was an Associate Editor in the IEEE Signal Processing Letters journal (2009-2012) and Senior Associate Editor in the IEEE Signal Processing Letters journal (2012-2014), while since mid 2012 he has been also Associate Editor in the IEEE Transactions on Image Processing journal. He is currently a Senior Associate Editor in the IEEE Transactions on Image Processing journal.  Over the same period, Dr. Tzovaras acted as ad hoc reviewer for a large number of International Journals and Magazines such as IEEE, ACM, Elsevier and EURASIP, as well as International Scientific Conferences (ICIP, EUSIPCO, CVPR, etc.). Since 1992, Dr. Tzovaras has been involved in more than 100 European projects, funded by the EC and the Greek Ministry of Research and Technology. Within these research projects, he has acted as the Scientific Responsible of the research group of CERTH/ITI, but also as the Coordinator and/or the Technical/Scientific Manager of many of them (coordinator of technical manager in 24 projects – 12 H2020, 1 FP7 ICT IP, 7 FP7 ICT STREP, 3 FP6 IST STREP and 1 Nationally funded project).

Beyond Silicon Valley: can Universities act as innovation powerhouses in a period of disruption?

mar15

by Marios Dikaiakos

Departement of Computer Science - University of Cyprus
Cyprus

Abstract

In this period of rapid scientific and technological progress, political, social and economic stakeholders around the world increasingly expect that universities act as driving forces of innovation-driven economic development at a national, regional or even international scale, pursuing aggressively the translation of their scientific results into commercialized products or services. These expectations are inspired primarily by the remarkable impact that American academia have had in the technological revolution of the post-World War II era, which led to unprecedented innovations in Information and Communication Technologies, the massive adoption of the Internet, and the rapid digitalization of every aspect of human activity.
Is it realistic to expect that universities around the world can successfully replicate the success of top American research universities and become innovation powerhouses that support the aspirations of their societies to compete in a globalized innovation arena? Does this aspiration align with the universities’ raison d’ être in the 21st century or is it just a pipe dream leading to misaligned priorities, an undermining of their true mission, and a waste of resources?
In this talk, I discuss these questions and explore the conditions wherein universities around the world can seek to become driving forces for “home-brewed” innovation with a certain level of success.

Biography

Marios D. Dikaiakos is Professor of Computer Science at the University of Cyprus. He was Head of the Computer Science Department between 2010-2014, and is the Founding Director of the Laboratory for Internet Computing (2001). He received his Ph.D. in Computer Science from Princeton University in 1994. His research focuses on Internet Computing, with recent activities focusing on Cloud Computing systems and Big Data. He has been a principal institutional investigator or co-principal investigator for over 30 funded R&D projects, published over 170 papers in books, international scientific journals and refereed conference proceedings, and was in charge of the development of several research software systems released internationally. Since January 2015, Professor Dikaiakos serves as the Founding Director of the Center for Entrepreneurship (http://c4e.org.cy) of the University of Cyprus, where he leads the design and implementation of the Center’s educational, support, outreach activities and its international relations.

Interesting Links