Prof. Giuliano Antoniol | 19.04.2016 | 11:00 Uhr | I.2.35

Abstract

The IEEE glossary defines traceability as “the degree to which a relationship can be established between two or more products of the development process.” Traceability underlies many important software and systems engineering activities, such as program understanding, change impact analysis, contractual agreement as well as verification and validation. Despite important research advances in the automated creation and maintenance of trace links, artifact traceability is still not completely understood.

This talk briefly introduces key traceability ideas; the basic information retrieval models and traceability recovery approaches. It briefly summarizes the state of the art in traceability recovery and recent advances. Finally it discusses an on going research effort to determine an optimal and minimal set of candidate links to build a robust traceability recovering recognizer. Surprisingly, it may happen that a system built with just a handful of carefully selected candidate links, outperforms systems built using much more information. The talk presents preliminary results on publicly available datasets and it discuss findings, implications and research directions.

Giuliano Antoniol is professor of Software Engineering in the Department of Computer and Software Engineering of the Polytechnique Montréal where he directs the SOCCER laboratory. He worked in private companies, research institutions and universities. In 2005 he was awarded the Canada Research Chair Tier I in Software Change and Evolution. He has served in the program, organization and steering committees of numerous IEEE and ACM sponsored international conferences and workshops. His research interest include software evolution, empirical software engineering, software traceability, search based software engineering, mining software repositories and software testing.

Bernhard Rinner | May 10, 2016 | 18:00 | Seeparkhotel (co-located with mmsys2016.itec.aau.at)

Abstract: Smart cameras combine video sensing, processing, and communication on a single embedded platform. Networks of smart cameras are real-time distributed embedded systems that perform computer vision using multiple cameras. This new approach has emerged thanks to a confluence of simultaneous advances in four key disciplines: computer vision, image sensors, embedded computing, and sensor networks. Recently these networks have gained a lot of interest in research and industry; applications include surveillance, assisted living and smart environments. In this talk I will introduce some fundamentals of the emerging field of smart camera systems, present selected examples and discuss trends and challenges for future systems.

Bio: Bernhard Rinner is professor at the Alpen-Adria-Universität Klagenfurt, Austria where he is heading the Pervasive Computing group. He is deputy head of the Institute of Networked and Embedded Systems and served as vice dean of the Faculty of Technical Sciences from 2008-2011. Before joining Klagenfurt he was with Graz University of Technology and held research positions at the Department of Computer Sciences at the University of Texas at Austin in 1995 and 1998/99.

His current research interests include embedded computing, sensor networks and pervasive computing.  Bernhard Rinner has been co-founder and general chair of the ACM/IEEE International Conference on Distributed Smart Cameras and has served as chief editor of a special issue on this topic in The Proceedings of the IEEE.  Currently, he is Associate Editor for Ad Hoc Networks Journal and EURASIP Journal on Embedded Systems. Together with partners from four European universities, he has jointly initiated the Erasmus Mundus Joint Doctorate Program on Interactive and Cognitive Environments (ICE). He is member of IEE and IFIP and member of the board of the Austrian Science Fund.

Gheorghita Ghinea | Brunel University, UK | May 10, 2016 | 19:00 | Seeparkhotel (co-located with mmsys2016.itec.aau.at)

Abstract: Traditionally, multimedia applications have primarily engaged two of the human senses ? the audio and the visual ? out of the five possible. With recent advances in computational technology, it is now possible to talk of applications that engage the other three senses, as well: tactile, olfaction, and gustatory. This integration leads to a paradigm shift away from the old multimedia towards the new mulsemedia : multiple sensorial media. In his talk, Dr. Ghinea is going to focus on the issue of the perceptual experience of mulsemedia and how research in the area has opened new and sometimes challenging opportunities for mulsemedia applications.

Bio: Dr. Gheorghita Ghinea is a Reader in the School of Information Systems and Computing at Brunel University, United Kingdom. He received the B.Sc. and B.Sc. (Hons) degrees in Computer Science and Mathematics, in 1993 and 1994, respectively, and the M.Sc. degree in Computer Science, in 1996, from the University of the Witwatersrand, Johannesburg, South Africa; he then received the Ph.D. degree in Computer Science from the University of Reading, United Kingdom, in 2000. His research activities lie at the confluence of Computer Science, Media and Psychology. In particular, his work focuses on the area of perceptual multimedia quality and building end-to-end communication systems incorporating user perceptual requirement. He has over 250 publications in leading international conferences and journals; Dr. Ghinea has co-edited two books on Digital Multimedia Perception and Design, and Multiple Sensorial Media Advance and Applications. He leads a team of 8 researchers and consults regularly to both public and private organisations and bodies in his areas of research expertise.

Omar Aziz Niamut | TNO | May 10, 2016 | 16:30 | Seeparkhotel (co-located with mmsys2016.itec.aau.at)

Abstract: This talk presents the Spatial Representation Description (SRD) feature of the second amendment of MPEG DASH standard part 1, 23009-1:2014. SRD is an approach for streaming only spatial sub-parts of a video to display devices, in combination with the form of adaptive multi-rate streaming that is intrinsically supported by MPEG DASH. The SRD feature extends the Media Presentation Description (MPD) of MPEG DASH by describing spatial relationships between associated pieces of video content. This enables the DASH client to select and retrieve only those video streams at those resolutions that are relevant to the user experience. The paper describes the design principles behind SRD, the different possibilities it enables and examples of how SRD was used in different experiments on interactive streaming of ultra-high resolution video.

Bio: Omar Aziz Niamut is a senior research scientist at the Netherlands Organization for Applied Scientific Research (TNO), working on shared interactive and immersive media delivery. He received MSc. (2001) and PhD. (2006) degrees from Delft university of Technology. In 2007, he advised and reported to the European Parliament and the European Commission on the harmonisation of mobile TV. From 2008-2010, he made over 300 standardization contributions to ETSI TISPAN and advised the Singapore government on the use of IPTV standards. In 2007, he was the technical lead for one of the world’s first user trial of social TV services and in 2014, he led a joint effort with the BBC towards the world’s first live tiled streaming of UHD video to end users. He has been active as work package lead in EU FP7 projects FascinatE, STEER and EXPERIMEDIA. He has presented at international industry events such as Broadband Home Forum, International Broadcast Convention, IPTV Seminar and IPTV World Forum, NEM Summit and Future Internet Assembly. He is an expert reviewer for the IWT funded iMinds ICON programme and a NEM ETP Steering Board member. He is author of multiple journal papers and conference publications in the fields of audio coding, interactive IPTV services and immersive media, filed 20 patent applications, contributed a chapter to a book on Social TV and co-edited a book on format-agnostic media. He has been serving as Co-chair and main organizer of the WSICC workshop 2013 and 2015, the workshop series (2013, 2014) on cross-breeding social networks and networked media in the Future Internet, and has been appointed as General Co-chair for ACM TVX 2017, the prime venue for publishing research related to TV and online video experiences.

Ali C. Begen | IEEE ComSoc Distinguished Lecturer | May 10, 2016 | 15:30 | Seeparkhotel (co-located with mmsys2016.itec.aau.at)

Abstract: Watching video over the Web is without a doubt the most popular way to access both free and premium content on connected devices for all kinds of consumers from novices to professionals. The underlying technology, called HTTP adaptive streaming, is quite straightforward; however, it does not always result in the most pleasant viewer experience due to quality fluctuations. This can cause dissatisfaction for consumers, leading to revenue loss for providers in both managed and unmanaged video services. In this talk, we will explain a new approach to adaptive streaming that we refer to as content-aware streaming. This is not just another adaptive streaming algorithm but a completely untraditional streaming toolset that enables premium viewer experience with minimal cost to the provider. After we briefly cover what solutions have been proposed for existing problems, and what worked and did not work for consumers and providers, we take a stab at clarifying some of the recent public announcements that have been largely misunderstood by the streaming community. Most importantly, we explain why content-aware encoding is not the solution by itself to the problems most providers are facing today.

Bio: Ali C. Begen has joined MediaMelon, Inc. as the principal architect for streaming technologies in February 2016, where he is currently heading the development efforts for MediaMelon’s content-aware streaming solutions that enable premium viewer experience while minimizing operational costs and improving profitability. He is also a co-founder of Networked Media, a technology company that offers consulting services to industrial, legal and academic institutions in the IP video space. He has been a research and development engineer since 2001, and has broad experience in mathematical modeling, performance analysis, optimization, standards development and intellectual property innovation. Between 2007 and 2015, he was with the Video and Content Platforms Research and Advanced Development Group at Cisco, where he has architected, designed and developed algorithms, protocols, products and solutions in the service provider and enterprise video domains. Since 2007, he has been leading professional and academic projects, and teaching graduate-level courses and giving lectures in universities around the world. Ali holds a Ph.D. degree in electrical and computer engineering from Georgia Tech. He received a number of scholar and industry awards, and he has editorial positions in prestigious magazines and journals in the field. He is a senior member of the IEEE and a senior member of the ACM. More recently, in January 2016, he was elected as a distinguished lecturer by the IEEE Communications Society. Further information on his projects, publications, keynotes, tutorials, and teaching, standards and professional activities can be found at https://ali.begen.net.

Oge Marques | Distinguished ACM Speaker | Florida Atlantic University (FAU) | May 10, 2016 | 14:00 | Seeparkhotel (co-located with mmsys2016.itec.aau.at)

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 include crowdsourcing 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.

Bio: Oge Marques (https://faculty.eng.fau.edu/omarques/) is Professor of Computer and Electrical Engineering and Computer Science at Florida Atlantic University (FAU) (Boca Raton, Florida). He has more than 25 years of teaching and research experience in the fields of image processing and computer vision. 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 the (co-) author of two patents, more than 100 refereed journal and conference papers, and several books in these topics, including the textbook Practical Image and Video Processing Using MATLAB (Wiley-IEEE Press, 2011). He is Editor-in-Chief (with Borko Furht) of the upcoming 3rd edition of the Encyclopedia of Multimedia (https://encyclopediaofmultimedia.com). He is a senior member of both the ACM and the IEEE and a member of the honor societies of Tau Beta Pi, Sigma Xi, Phi Kappa Phi, and Upsilon Pi Epsilon.

Muhammad Taimoor Khan, MSc. PhD. | 29.02.2016 | 11:00 Uhr | E.2.69

Abstract

In this talk, we present a rigorous behavior based approach to develop reliable, secure and resilient application software for industrial control systems (in particular). The goal here is to employ formal methods to first build application right and then to continuously monitor the application for security and resilience.
To achieve the goal, we first develop correct-and-secure-by-construction application software using theorem proving (i.e. prover Coq) through refinement and synthesis of abstract data types. Then we introduce a run-time security monitor for application software, which detects both known and unknown computational cyber attacks. For resilience, we employ dependency directed reasoning to recover the system in a safe state, if any inconsistency is detected. Our security monitor is sound and complete, eliminating false alarms, as well as efficient, supporting real-time systems. In contrast, conventional run-time security monitors for application software either produce (high rates of) false alarms (e.g. intrusion detection systems) or limit application performance (e.g. run-time verification systems).
Our run-time monitor detects attacks by checking the consistency between the application run-time behavior and its expected behavior modeled in its specification. Our specification language is based on monadic second order logic (i.e. first order logic and set theory) and event calculus interpreted over algebraic data structures; application implementation can be in any programming language. Based on our defined denotational semantics of the specification language, we prove that the security monitor is sound and complete, i.e. it produces an alarm if and only if it detects an inconsistency between the application execution and the specified behavior. Importantly, the monitor detects not only cyberattacks but all behavioral deviations from specification, e.g. bugs, and so, is readily applicable to the security of legacy systems.
Finally, we present the evaluation of the monitor in the industrial control systems security domain, specifically in water management, demonstrating that run-time, sound and complete monitors employing verification techniques are effective, efficient and readily applicable to demanding real-time critical systems, without scalability limitations.

Muhammad Taimoor Khan is a postdoctoral researcher at Qatar Computing Research Institute (jointly with CSAIL, MIT, USA), Qatar. His current research is to develop reliable, secure and resilient software by the application of formal methods. On one hand, his project is focused on developing a tool to automatically detect and correct, known and unknown attacks through monitoring behavioral inconsistencies between specification and execution at run-time. On the other hand, his another project is focused on using theorem prover as a programming language to develop correct-and-secure-by construction software.
Prior to this, Taimoor Khan has passed doctoral studies at Research Institute for Symbolic Computation, Hagenberg, Austria with All-Distinctions in 2014. His PhD dissertation was about formal specification and verification of computer algebra software. Before joining RISC, he graduated in MSc Advanced Distributed Systems from the University of Leicester, UK with Distinction in 2008. As a final semester project he worked on the model-based verification of the various communication protocols of NASA in the frame of project “Space Link Extension Service Management”. Also prior to this, he completed his M.Sc. in Computer Science from Pakistan in 2001 and then worked for about five years in the software industry specializing in Java (EE/ME), XML and Web Services.
Taimoor Khan has been visiting scientist at various international reputed institutes including CSAIL, MIT, USA and ENSIIE, INRIA, France. Also he has won various research awards including the best student paper award at the most premier conference in computer algebra (CICM) in 2012.
He is also working as an associate tutor at University of Leicester, UK. Here he is teaching different courses (e.g. Domain Specific Languages) to MSc students (DL) and supervising their final semester projects. Prior to that, he has also taught undergraduate and graduate students at numerous universities in Pakistan as an assistant professor for several years.

Faisal Z. Qureshi | 13.01.2016 | 10:00 Uhr | Seminarraum Lakeside Labs B4.1.114

Abstract:

We developed a new method for extracting 3D flight trajectories of droplets using high-speed stereo capture. We noticed that traditional multi-camera tracking techniques fare poorly on our problem, in part due to the fact that all droplets have very similar shapes, sizes and appearances. Our method uses local motion models to track individual droplets in each frame. 2D tracks are used to learn a global, non-linear motion model, which in turn can be used to estimate the 3D locations of individual droplets even when these are not visible in any camera. We have evaluated the proposed method on both synthetic and real data and our method is able to reconstruct 3D flight trajectories of hundreds of droplets. The proposed technique solves for both the 3D trajectory of a droplet and its motion model concomitantly, and we have found it to be superior to 3D reconstruction via triangulation. Furthermore, the learned global motion model allows us to relax the simultaneity assumptions of stereo camera systems. Our results suggest that, even when full stereo information is available, our unsynchronized reconstruction using the global motion model can significantly improve the 3D estimation accuracy.

Bio:

Faisal Qureshi is an Associate Professor of Computer Science at the University of Ontario Institute of Technology (UOIT), Oshawa, Canada. He obtained a PhD in Computer Science from the University of Toronto in 2007.  He also holds an M.Sc. in Computer Science from the University of Toronto, and an M.Sc. in Electronics from Quaid-e-Azam University, Pakistan.  Prior to joining UOIT, he worked as a Software Developer at Autodesk. His research interests include sensor networks, computer vision, and computer graphics. He has also published papers in space robotics.  He has interned at ATR Labs (Kyoto, Japan), AT&T Research Labs (Red Bank, NJ, USA), and MDA Space Missions (Brampton, ON, Canada).  He is a member of the IEEE and the ACM.

Alexander Beck | 10.12.2015 | 15:00 Uhr | HS 4

Kurzfassung

IT-Systeme sind stark zusammenhängende komplexe Strukturen, so dass eine Fokussierung auf das IT-System im Ganzen nur eine bedingt effiziente Sicherheitsbewertung gewährleistet. Die Sicherheit dieser komplexen Systemlandschaften stets aktualisiert unter Berücksichtigung aller Wechselbeziehungen zwischen Komponenten dieser Systemlandschaften darzustellen, gestaltet sich mangels geeigneter Modelle schwierig. Neben diesen Modellen bilden Schwachstellenbewertungen die Grundlage für die Bewertung der Sicherheit und werden durch aufwändige Betrachtungen interpretiert, um eine Gesamtbewertung zu ermitteln. Will man dieses Vorgehen automatisieren, muss zunächst eine gemeinsame Repräsentation von Schwachstellenbewertungen vereinbart werden. Das dazu geeignete Common Vulnerability Scoring System (CVSS) ermöglicht die Bewertung einzelner Schwachstellen hinsichtlich verschiedenster Fragestellungen. Um eine Darstellung der Gesamtsicherheit zu erreichen, müssen diese Schwachstellen aggregiert werden. Unter einer Aggregation ist dabei die gemeinsame Interpretation aller im Fokus stehenden Schwachstellen zur Erreichung einer Gesamtbewertung zu verstehen, welche auf Basis eines neuronalen Netzwerkes erfolgt. Das neuronale Netz ist ein lernfähiges Konzept der Informatik, mit dem es möglich ist auf Basis definierter Eingabeparameter ein definiertes Ergebnis zu modellieren. Dabei wird das Netz trainiert und die Ergebnisse mit Ergebnissen der bisherigen manuellen Bewertungsverfahren verglichen, bis eine entsprechende Qualität der automatisch ermittelten Ergebnisse gesichert ist.

Alexander Beck ist seit 2011 bei der Volkswagen AG tätig. Zuvor studierte er Informatik an Hochschule Harz und Otto-von-Guericke-Universität Magdeburg unter anderem mit den Schwerpunkten Datenintensive Systeme und Sicherheit. Im Rahmen seiner Dissertation erforscht er Verfahren zur automatisierten Sicherheitsbewertung von komplexen heterogenen IT-Infrastrukturen auf Basis neuronaler Netze.
Beruflich war er mehrere Jahre in der Informationssicherheit im Volkswagen Konzern tätig und beschäftigte sich mit den Themen Authentifizierung und Verschlüsselung. Aktuell arbeitet er im Bereich IT Projekt- und Programmmanagement der Volkwagen Financial Services AG und verantwortet IT Projekte im In- und Ausland.

Dr. Christian Beecks | 24.11.2015 | 16:00 Uhr | E.2.42

Abstract

With the advent of social networks and the advancement of powerful internet-enabled mobile devices, millions of users are able to easily generate, process, and share multimedia data at billion-scale every single day. The resulting multitude and versatility of multimedia data made available in the Internet challenge todays’ data management and analysis algorithms. In many research and application areas including information retrieval, data mining, and computer vision, users are no longer satisfied with keyword-based access but want to search, browse, explore, and analyze multimedia data according to content-based characteristics. One fundamental operation underlying many data analysis algorithms is similarity search which aims at retrieving the most similar multimedia objects with respect to a query. In order to carry out similarity search for query-like multimedia objects, the way of modeling similarity is of major significance due to its impact on efficiency and effectiveness.

In this talk, I will present my ongoing research in this fascinating field and highlight future research directions. More specifically, I will show how to approach similarity between multimedia data objects by means of gradient-based signatures in order to facilitate data analysis with high efficiency and efficacy.

Christian Beecks is a postdoctoral researcher in the data management and data exploration group at RWTH Aachen University, Germany. His research interests include efficient and adaptive multimedia data analysis, distance-based multimedia indexing and query processing, and real-time data management.