Einladung zur Antrittsvorlesung:

Antritt Elmenreich 13

Software Product Lines (SPLs) are families of related software systems whose members offer different combinations of features. SPL practices have proven extensive economical and technological benefits and are becoming more pervasive in domains where systematic and disciplined software reuse is fundamental to keep up with customer demands. Consequently, SPL testing has received increasing attention both by researchers and practitioners whose main challenge is how to effectively and efficiently cope with the typically large number of feature combinations. In this talk, I will present an overview of how Search Based techniques have been deployed to tackle this problem, describe some of our ongoing work on combinatorial interaction testing and lay out some open questions that are venues for further research or collaboration.

Dr. Roberto Erick Lopez-Herrejon is currently a Lise Meitner Fellow (2012-2014) sponsored by the Austrian Science Fund (FWF) at the Johannes Kepler University in Linz Austria . Additionally, since 2008 he is an External Lecturer at the Software Engineering Masters Programme of the University of Oxford, England. From 2010-2012 he held an FP7 Intra-European Marie Curie Fellowship on a project for consistency and composition of variable systems with multi-view models.  He obtained his PhD from the University of Texas at Austin in 2006, funded in part by a Fulbright Fellowship sponsored by the U.S. State Department. From 2005 to 2008, he was a Career Development Fellow at the Software Engineering Centre of the University of Oxford sponsored by Higher Education Founding Council of England (HEFCE). His expertise is software product lines, variability management, feature oriented software development, model driven software engineering, and consistency checking.

Energy Harvesting (EH) is a new paradigm in Wireless Sensor Networks (WSNs): sensor nodes are powered by energy harvested from the ambient, rather than by non-rechargeable batteries, thus enabling a potentially perpetual operation of the WSN. However, Energy Harvesting poses new challenges in the design of WSNs, in that energy availability is random and fluctuates over time, thus calling for radically different energy management solutions. In this talk we investigate the following fundamental question: how should the harvested energy be managed to ensure optimal performance? First, we consider a sensor powered by EH which senses data of varying importance and reports them judiciously to a Fusion Center. Assuming that data transmission incurs an energy cost, our objective is to identify low-complexity policies that achieve close-to-optimal performance, in terms of maximizing the average long-term importance of the reported data. We first consider schemes that rely on the assumption of perfect knowledge of the amount of energy available in the battery. Subsequently, we investigate the design of operation policies that maximize the long-term reward under imperfect knowledge of the State-Of-Charge (SOC). Moreover, for both scenarios, we explore the impact of time-correlation in the EH process, showing that simple adaptation to the state of the EH process yields close-to-optimal performance, without requiring full knowledge of the SOC of the battery.

Michele Zorzi is a Professor at the Department of Information Engineering of the University of Padova. Prior to his current appointment, he was employed at the Politecnico di Milano, the University of Ferrara and the University of California at San Diego, with which he still has an active collaboration. He is an IEEE Fellow. His main research interests are in the area of wireless communications and networking, sensor networks and IoT, underwater communications and networks, cognitive networking, and energy-efficient protocol design. His work is widely cited, with a total of more than 11000 citations and an h-index of 52.

Abstract: It is now well known that many problems in image understanding and multimedia content analysis can benefit from human computations and crowdsourcing techniques. In this talk, we’ll first examine a few influential works from the literature where explicit tasks are performed by motivated workers (through altruistic incentives or personal enjoyment) and then combined with content analysis methods. In the second part of the presentation, our recent results on ‘zoomable video players’ will be briefly presented. These examples particularly highlight the use of implicit tasks to naturally infer knowledge about the visual or multimedia content.

Short-CV: Vincent CHARVILLAT received the Eng. degree in Computer Science and Applied Mathematics from ENSEEIHT, Toulouse France and the M.Sc. in Computer Science  from the National Polytechnic Institute of Toulouse, both in 1994. He received the Ph.D. degree in Computer Science from the National Polytechnic Institute of Toulouse in 1997. He is currently a full professor at the University of Toulouse, IRIT research lab, ENSEEIHT Eng. School. Vincent CHARVILLAT is the head of  VORTEX research team at ENSEEIHT. His main research interests are visual processing and multimedia applications. Current topics of research include visual object processing, visual compositing, visual interaction design and crowdsourcing in multimedia.