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.

Abstract:

The ubiquitous access to the Web and, more generally to digital contents has dramatically changed the “digital experience” everyone daily lives. However, the diversity of contexts (technical contexts, social contexts, personal contexts), the diversity of users, the diversity of digital contents have definitely made mandatory the (or at least, some) personalization of the users’ experience.
The notion of “Universal Multimedia Experience (UME)” has been coined to represent this attempt to put the user at the center of the content delivery process. The key idea that underpins this concept is the notion of adaptation: adaptation of the delivery process to the network conditions, adaptation of the type of content to the user’s preferences and profile, adaptation of the content itself to the user’s terminal capabilities and user’s activity, etc.
This talk will address some of the key issues related to multimedia content adaptation and multimedia quality of experience.
First, we will focus on the modeling of the user’s context. Indeed, a basic condition to adaptation is the capture (or the inference) and the representation of the user’s “context” i.e., the technical conditions (network, terminal), the user’s activity, the user’s intention, the user’s profile and preferences.
Then, the core of the talk will be dedicated to the specific issues of video content adaptation. By their isochronous nature; by the richness and complexity of the information they carry; by the multiplicity of their stakeholders (end user, creator, video broadcaster), video contents raise specific and complex issues related to both semantic, perceptual and even legal constraints and conflicts.
To address these issues, we will introduce the concept of Utility Function, which aims at integrating in one multi-dimensional optimization space, semantic constraints, user’s context characteristics and perceptual considerations.
As a side effect of this modeling of the video adaptation process, we will analyze how the enforcement of access control rules on video contents can be formalized as a problem of content adaptation.
Finally, we will investigate some still open issues related to video delivery optimization in P2P and delay-tolerant networks, complex multi-dimensional semantic constraints, visual aesthetics, privacy and security.
This talk will be illustrated by research works developed in the International Doctoral College and Federative Laboratory “Multimedia Distributed and Pervasive Secure Systems” (MDPS) which comprises researchers from the National Institute of Applied Sciences (INSA) of Lyon, France (Pr. L. Brunie’s team), the University of Passau (Pr. H. Kosch’s team), Germany and the University of Milan, Italy (Pr. E. Damiani’s team).

CV:

Lionel Brunie is full professor at the National Institute of Applied Sciences (INSA) of Lyon, France, where he leads the LIRIS DRIM research team.
With 10 permanent researchers and 15+ PhD students, the DRIM team is specialized in distributed data management, multimedia information systems, information retrieval, and security/privacy.
In 2007, along with Pr Harald Kosch (University of Passau, Germany), Lionel Brunie created the French-German doctoral college in “Multimedia Distributed and Pervasive Secure Systems (MDPS)”. MDPS was extended in 2009 to the University of Milan, Italy (Pr. Ernesto Damiani’s team). MDPS proposes both a framework for international co-supervised PhDs and a federative research institute that develops a joint research agenda.
Lionel Brunie’s main topics of interest include: data management in large scale and pervasive systems, collaborative multimedia information systems, security and privacy, e-health applications.

Der Rückblick zum TEWI-Kolloquium von Dr. Oge Marques am 21-05-2013 beinhaltet die Videoaufzeichnung sowie die Folien:

Video

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Slides

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Abstract:
There are many challenging problems in computer vision for which state-of-the-art solutions fall short of performing perfectly. The realization that many of these tasks are arduous for computers yet are relatively easy for humans has inspired many researchers to approach those problems from a human computation viewpoint, using methods that includecrowdsourcing and games – often called ‘games with a purpose’ (GWAPs). The talk discusses how we can use human computation (in general) and particularly games to help uncover hidden aspects of visual perception and use these findings to improve computer vision solutions to related problems. It particularly highlights two examples of our recent work on the topic:
1.   Guess That Face (with Mathias Lux and Justyn Snyder, CHI 2013): a face recognition game that reverse engineers the human biological threshold for accurately recognizing blurred faces of celebrities under time-varying conditions.
2.   Ask’N’Seek (with Vincent Charvillat and Axel Carlier, ECCV 2012): an object detection and labeling game that asks users to guess the location of a hidden region within an image with the help of semantic and topological clues and uses the information collected from game logs, combined with results from content analysis algorithms, to feed a machine learning algorithm that outputs the outline of the most relevant regions within the image and their names.

Short CV:
Oge Marques is an Associate Professor in the Department of Computer & Electrical Engineering and Computer Science at Florida Atlantic University (FAU) (Boca Raton, Florida). He received his Ph.D. in Computer Engineering from FAU in 2001. He has more than 20 years of teaching and research experience in the fields of image processing and computer vision, in different countries (U.S., Austria, Brazil, Netherlands, Spain, France, and India), languages (English,Portuguese,Spanish), and capacities.He is the (co-) author of more than 50 refereed journal and conference papers and several books in these topics, including the textbook Practical Image and Video Processing UsingMATLAB (Wiley, 2011). His research interests are in the area of intelligent processing of visual information, which combines the fields of image processing, computer vision, image retrieval, machine learning, serious games, and human visual perception. He is particularly interested in the combination of human computation and machine learning techniques to solve computer vision problems.

He is a senior member of both the ACM and IEEE, and a member of the IEEE Computer Society, IEEE Education Society, IEEE Signal Processing Society, and the honor societies of Tau Beta Pi, Sigma Xi, Phi Kappa Phi, and Upsilon Pi Epsilon.

Der Rückblick zum TEWI-Kolloquium und Antrittsvorlesung von Prof. Dr. Martin Pinzger am 24.04.2013 beinhaltet die Videoaufzeichnung sowie die Folien:

Video

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Slides

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Abstract: Software is omnipresent. It is key to successful businesses and has become key to our social activities. As many systems, also software systems need to change in order to stay successful on the market. However, these changes cause software systems to become larger in size and more complex as described by Lehman’s Laws of Software Evolution. As a consequence, more resources are needed to maintain, or in general, evolve a software system. Evolving software systems is therefore mastering change and system complexity. The goal of my research and teaching is to provide software engineers with means to master this challenge.

In this inauguration lecture, I outline several challenges of evolving software systems and present the ideas and findings from my recent research to address them. In particular, I show how we can use the history of software projects to identify critical parts of a software system and how we can use visualization techniques to help software engineers to understand the implementation of large, complex software systems including large spreadsheets.

CV: Martin Pinzger is Professor of Software Engineering and head of the Software Engineering Research Group at the University of Klagenfurt. His research interests cover various topics in designing and evolving software systems. He holds a PhD in Computer Science from the Vienna University of Technology, worked as a Senior Research Associate at the University of Zurich, and as an Assistant Professor at the Delft University of Technology. He is a recipient of the prestigious Dutch NWO Vidi grant and co-founder of the TU Delft start-up Infotron.