20/Feb/2020 | 09:30 - 13:00

Polo Ferrari 1, aula Ofek

Via Sommarive 5, Povo

Entrambi i seminari saranno tenuto in lingua inglese.

Collaborative augmented reality: technical, collaboration and social issues

Joel Lanir

20/Feb/2020 | 09:30 – 11:00
Aula Ofek

Abstract
Collaborative Augmented Reality (AR) systems enable multiple people to share the same augmented view, with collaborators being either collocated or remote. These systems are currently at a critical point in time as they are soon to become more commonplace. However, AR technology has only recently matured to the point where researchers can focus on the nuances of supporting collaboration, rather than needing to focus on creating the enabling technology. Leading companies such as Apple and Microsoft are racing to bring new and better AR hardware to the market. Among the possible applications, it is widely viewed that collaborative AR is to be one of the killer applications. The possibilities of collaborative AR are tremendous. From the ability to show a proxy of oneself in order to provide the sense of being there, to using AR in remote collaboration around a physical object (e.g., to help fix a technical problem), or simply using AR in a shared game (such as Pokemon Go). In this talk, I will survey existing collaborative AR topics and describe some of the works that I was involved with that study how to better support collaboration in AR as well as look at various social issues surrounding collaborative AR.

About the Speaker
Joel Lanir is a senior lecturer and coordinates the Human-Computer Interaction and Visualization Lab at the University of Haifa, Israel. His research interests focuses on the design, implementation and evaluation of novel technologies, and well as on the study and understanding of how technologies affect human behavior. In 2017, the laboratory has received an ISF grant to examine children’s collaborative learning in the museum setting.


Future Internet: a bridge from Artificial Intelligence to Quantum Intelligence

Antonio Manzalini

20/Feb/2020 | 11:30 – 13:00
Aula Ofek

Abstract
The transformative role of Computer Science and Information Communication Technologies (ICT) has always been witnessed as a precursor of scientific progress and economic growth in the modern world.  Today, like never before, we are witnessing an increasing exploitation of ultra-broadband (fixed-mobile) infrastructures and Cloud-Edge Computing, for providing any sort of digital services for our daily life.
The so-called Digital Transformation is going to create a pervasive artificial nervous system embedded into the reality around us. As a matter of fact, a very large adoption of mobile terminals and devices and a pervasive distribution of the Internet of Things will allow collecting enormous big data sets, which will be communicated and transmitted by ultra-broadband, low latency network connections (e.g., 5G) to Cloud and Edge Computing Data Centers (DCs). These DCs, in turn, will store and elaborate the collected big data in order to infer decisions (with Big Data analytics and big data analytics methods) which will be then exploited, locally, by actuators. This is the typical closed loop of any autonomic or artificial nervous system (e.g., collect-analyze-decide-act). Therefore, big data analytics and AI will be at the center of this transformation.
Looking at the most promising Artificial Intelligence technological approaches, Deep Neural Networks (DNNs) are outperforming in several applications domains. Processing big data to infer patterns at high speeds and with low power consumption is becoming more and more a central technological challenge of the Digital Transformation. At the same time, electronics starts facing physically fundamental bottlenecks whilst nanophotonics technologies are considered promising candidates to overcome electronics limitations.
In this direction, there are evidences of an emerging research an innovation field, rooted in quantum optics, where the technological trajectories of DNNs and nanophotonics are crossing each other. As a matter of fact, it is likely that one of the possible explanations of DNN outperforming is really deeply rooted in the principles of theoretical Physics, specifically Quantum Field Theory (QFT) and Gauge theory.  
This is encouraging even more researches and experiments in the direction of a full exploitation of quantum computing and networking for the development of future internet and innovative ICT solutions. Given that QFT and Gauge theory have been already proposed for modelling the brain and biological nervous systems, this talk explores the intriguing possibility of exploiting quantum optics principles also for developing a future “nervous system” for the coming Quantum Society.

About the Speaker
Antonio Manzalini received the M. Sc. Degree in Electronic Engineering from the Politecnico of Turin (Italy) and the Ph.D in Computer Science and Networks from Télécom SudParis and Université Pierre & Marie Curie – Sorbonne Universités (France). In 1990 he joined Telecom Italia (CSELT) to develop transport networks architectures. He has been ITU Rapporteur (1996-2000). He has been involved in leading roles of several EURESCOM and EU-funded Projects (e.g., FP5 IST Project LION, FP6 IST IP NOBEL, FP6 FET ICT Project CASCADAS). In 2003 he has been appointed as member of the Scientific Committee of the Centre Tecnològic de Telecomunicacions de Catalunya. In 2008 he achieved the International Certification of Project Manager by PMI. He has been the leader of two EIT-Digital funded projects on SDN and NFV infrastructures (2010-2012). In 2013 he has been the Chair of the IEEE initiative on Software Defined Networks (2013-2016). Currently, he is joining the Board of IEEE Comsoc Industry Committee. He has been General Chair and TPC member of  several IEEE Conferences. He owns seven patents. His results have been published in more than 130 technical papers and publications. His interests in TIM concern SDN, NFV, Cloud vs Multi-Access Edge Computing for 5G, HPC, Future Internet, Quantum Communications and Metamaterials (for 6G).