Prof. Tsvi Kuflik | 8th October 2015 | 16:00 | E.1.42

Abstract:
Over the years, the area of user modeling (and later on recommendation systems) produced a variety of user modeling techniques. These techniques were developed for modeling and representing the users in order to better understand their needs and provide them with personalized services. The common techniques in use are collaborative filtering and content/feature based, while in specific domains we can find also case-based, demographic and overlay approaches. However, the knowledge represented by these techniques is quite limited. In recent years, with the advent of web 2.0 and the social and semantic web, personal information becomes widely available online in various forms. This poses opportunities as well as major challenges for the classical user modeling approaches – how to make use of this information to enhance user modeling? As a potential solution to the problem, we are exploring the idea of graph-based user modeling representation, as an integrative framework that enables standard and simple representation of users‘ characteristics, not limited to a specific technique. In various studies we demonstrated the potential benefits of this approach and it’s possible contribution to user modeling and recommender systems. The talk will briefly present the general idea of graph-based user modeling as well as research results that demonstrate its contribution to a variety of domains and scenarios.

Short c.v.
Prof. Tsvi Kuflik heads the Information Systems Dept. at The University of Haifa. Over the past ten years, the focus of his work was on ubiquitous user modeling applied to cultural heritage. In the course of his work, a “Living Lab” has been developed at the University of Haifa – a museum visitors’ guide system was developed for the Hecht museum. It is available for visitors on a daily basis and serves also as a test bed for experimenting with novel technologies in the museum. Currently, the system is being used for research on Social Signal Processing where signals transmitted by devices carried by the visitors are used for modeling group behavior, in order to reason about the state of the group visit. Another research direction focusses on the use of intelligent user interfaces in ubiquitous computing within the “living lab”. Where issues like interaction with large, situated displays; interrupt management; navigation support; temporal and lifelong aspects of ubiquitous user modeling are studied. Tsvi got BSc. and MSc. In computer science and PhD. In information systems from Ben-Gurion University of the Negev, Israel. Over the years Tsvi collaborated with local and international researchers, supervised graduate students working with him on this research, organized the PATCH workshops series (Personal Access To Cultural Heritage) and published about 200 scientific papers, out of them 30 papers about this specific research. Tsvi is also a distinguished ACM scientist and a senior IEEE member.

Markus Schedl | Thursday, 1st October 2015 | 14:00 | E.2.42

Abstract:
Social media represent an unprecedented source of information about every topic of our daily lives. Since music plays a vital role for almost everyone, information about music items and artists is found in abundance in user-generated data. In this talk, I will report on our recent research on exploiting social media to extract music-related information, aiming to improve music retrieval and recommendation. More precisely, I will elaborate on the following questions:

• Which factors are important to human perception of music?
• How to extract and annotate music listening events from social media, in particular microblogs?
• What can this kind of data tell us about the music taste of people around the world?
• How to make accessible music listening data from social media in an intuitive way?
• How to build music recommenders tailored to user characteristics?

Bio:
Markus Schedl is an associate professor at the Johannes Kepler University Linz / Department of Computational Perception. He graduated in Computer Science from the Vienna University of Technology and earned his Ph.D. in Technical Sciences from the Johannes Kepler University Linz. Markus further studied International Business Administration at the Vienna University of Economics and Business Administration as well as at the Handelshögskolan of the University of Gothenburg, which led to a Master’s degree.
Markus (co-)authored more than 100 refereed conference papers and journal articles (among others, published in ACM Multimedia, SIGIR, ECIR, IEEE Visualization; Journal of Machine Learning Research, ACM Transactions on Information Systems, Springer Information Retrieval, IEEE Multimedia). Furthermore, he is associate editor of the Springer International Journal of Multimedia Information Retrieval and serves on various program committees and reviewed submissions to several conferences and journals (among others, ACM Multimedia, ECIR, IJCAI, ICASSP, IEEE Visualization; IEEE Transactions of Multimedia, Elsevier Data & Knowledge Engineering, ACM Transactions on Intelligent Systems and Technology, Springer Multimedia Systems).
His main research interests include web and social media mining, information retrieval, multimedia, and music information research.
Since 2007, Markus has been giving several lectures, among others, „Music Information Retrieval“, „Exploratory Data Analysis“, „Multimedia Search and Retrieval“, „Learning from User-generated Data“, „Multimedia Data Mining“, and „Intelligent Systems“. He further spent guest lecturing stays at the Universitat Pompeu Fabra, Barcelona, Spain, the Utrecht University, the Netherlands, the Queen Mary, University of London, UK, and the Kungliga Tekniska Högskolan, Stockholm, Sweden.

Contact Details:
Dr. Markus Schedl
Deptartment of Computational Perception
Johannes Kepler University
Altenberger Straße 69
4040 Linz, Austria
Tel.: +43 732 2468 1512
e-mail: markus.schedl@jku.at
Website: http://www.cp.jku.at/people/schedl
Full publication record is available at http://www.cp.jku.at/people/schedl/publications.html

Abstract:

In recent years many times sustainability and renewable energy consumption have been set on the agenda. However, the pressing issue how to make people reduce their amount of energy consumed – or their switching  towards green alternatives – has received far less research attention. The academic discipline of behavioral economics has much to offer to this debate. In the presentation we will summarize prior research on the role of individual differences and various pricing and framing techniques that have proven to be helpful in making people switch to green energy. We will also address challenges and future directions in behavioral energy economics.

Bio:

Laurens Rook is Assistant Professor at Delft University of Technology, the Netherlands. He received his Ph.D. from the Erasmus University Rotterdam (in 2008), and his bachelor and master’s degrees from the  University of Amsterdam, the Netherlands (in 2001; MA Thesis on Mass Psychology in Fine Art and Advertising).  His research focuses on herd and imitative behavior in creative context, and is published in the Creativity Research Journal.  His second focus is on behavioral informatics. Laurens collaborates with the Learning Agents Research Group at Erasmus (LARGE). A recent paper on using social media apps to make people consume green energy  (together with University of Connecticut, USA) was awarded best poster  award (2nd prize, the 2014 Conference on Information Systems and Technology).  He lectures on Research Methodology,   Statistics, and Group Dynamics, but also is a graduated professional artist (Academy of Arts Rotterdam, 1997) with collected work in the Municipal Archives of Rotterdam, the Netherlands, and the National Art Collection of Ireland.

Abstract:
Well-studied and commonly used in database query languages, aggregates form an intuitive means of representing knowledge. Since logic programs can be viewed as a database query language, it is natural to consider aggregates also in their context. However, logic programs allow for recursive specifications, which are either disallowed or allowed only in a restricted form in most database query languages. It turns out that aggregates used in connection with recursion raise a number of semantic issues, some of them reminiscent of issues with the semantics of negation, a hot topic in logic programming about three decades ago. One of the semantics for logic programs with negation that emerged and has proved itself to be viable is the stable model semantics or answer set semantics. In this talk we will show how ideas from this semantics can be transferred to programs with aggregates, what issues arise when doing so, and what options there are to overcome these. Furthermore, we will outline properties of the resulting semantics and the availability of system support. Finally, we show that all of these considerations are not really limited to aggregates, but more general constructs, such as those found in HEX programs or description logic programs.

Bio:
Wolfgang Faber serves as Professor of Artificial Intelligence at the University of Huddersfield (UK). Before that, he was a Reader at the same university, an Associate Professor at the University of Calabria (Italy), and an Assistant Professor at the Vienna University of Technology, where he also obtained his PhD in 2002. From 2004 to 2006 he was on an APART grant of the Austrian Academy of Sciences. His general research interests are in knowledge representation, logic programming, nonmonotonic reasoning, planning, and knowledge-based agents. He has published more than 100 refereed articles in major journals, collections and conference proceedings in these areas. He is one of the architects of DLV, a state-of-the-art system for computing answer sets of disjunctive deductive databases, which is used all over the world. He has acted as a chair for several workshops and conferences, and has been on the program committees of many of the major conferences of his research areas, and has served on the editorial board and as a reviewer for many journals and conferences on Artificial Intelligence, Knowledge Representation, and Logic Programming.

Abstract:

Business Process (BP) technology has emerged as one of the leading technologies in modeling, redesigning, and executing organisational processes in several different application domains. Among them, the representation and management of health and clinical processes have been attracting a growing interest. Such processes are in general related to the way each health organization provides the required healthcare services. Health and clinical processes underlie the specification and application of clinical protocols, clinical guidelines, clinical pathways, and the most common clinical/administrative procedures. Current BP systems are lacking in effective management of three general key aspects that are common (not only) in the clinical/health context: data dependencies, exception handling, and temporal constraints.
In this talk we will first introduce and discuss recent advances in business process modeling with respect to the healthcare/medical domain. Then, we will introduce some recent results on algorithms for checking temporal properties of business processes in presence of explicit temporal constraints among tasks.

Bio Carlo Combi:

In 1987 he received the Laurea Degree in E.E. by the Politecnico of Milan. In 1993 he received the Ph.D. degree in biomedical engineering. Since November 2001, he is with the Department of Computer Science of the University of Verona: from November 2001 to February 2005, he was Associate Professor of Computer Science; since March 2005, he is Professor of Computer Science. From October 2007 to September 2012 he was head of the Computer Science Department. Main research interests are related to the database and information system field, with an emphasis on the management of clinical information. The two main areas are temporal information systems (time-oriented data and process modelling) and multimedia databases. He is author of more than 100 papers published on international journals and proceedings of international conferences. He is author, with Elpida Keravnou – University of Cyprus and Yuval Shahar – Ben-Gurion University of the Negev, of the book „Temporal Information Systems in Medicine“, Springer, 2010. He is involved in the scientific activity of several scientific international journals and conferences. Since January 1999 he is editorial Board Member, journal Artificial Intelligence in Medicine. Since July 2009 he is chair of the Artificial Intelligence in Medicine Society (AIME). He is guest editor of several special issues of international journals (Methods of Information in Medicine, Annals of Mathematics and Artificial Intelligence, Artificial Intelligence in Medicine, Journal of Intelligent Information Systems, Computers in Biology and Medicine, ACM Transactions on Intelligent Systems and Technology).

Bio Roberto Posenato:

He took a degree in Computer Science in 1991 and a doctor’s degree in Computational Mathematics in 1996 at the University of Milan (Italy). Since November 2000, he is assistant professor at the same department. He has been lecturer for some courses in the theory of algorithms and computational complexity since 1996. Main research interests are related to approximation algorithms for combinatorial optimization problems, with emphasis for graph-based problems; moreover, he is interested into the study of time reasoning in workflow/business process systems and in temporal constraint networks. He is reviewer for national and international journals, magazines, and conferences and he has been involved in several national research projects.

Abstract:
Side-channel analysis have been introduced in the late nineties by Kocher et al. [1] to recover the secret keys of cryptographic implementations by exploiting the information leaked over side-channels. For embedded cryptographic devices typical side-channels are represented by the power consumption or by the electro-magnetic (EM) field emanations of physical implementations when executing cryptographic algorithms. Over the last two decades a lot of side-channel attacks have been developed and a variety of countermeasures have been proposed in literature to thwart side-channel analysis. Nevertheless, the quest after improved attacks and countermeasures is still a very active area of research, as testified by the many conferences and recent developments. In this talk, we provide an introduction to side-channel attacks covering some main topics like measurement setup, leakage models and statistical analysis. Then, we provide an overview on typical countermeasures against side-channel attacks covering different level of abstractions (circuit, algorithmic and protocol level).

[1] P. Kocher, J. Jaffe, and B. Jun. Differential Power Analysis. In M.J. Wiener, editor, Advances in Cryptology – CRYPTO ?99, volume 1666 of Lecture Notes in Computer Science, pages 388-397. Springer, 1999.

CVs:
Hermann Seuschek studied electrical engineering and information technology at Technische Universität München. Afterwards he worked for the central research and development department within the Siemens AG on topics related to applied cryptography and security for embedded systems. He left Siemens and joined the Institute for Security in Information Technology at Technische Universität München to pursue research in the field of automated side channel hardening of cryptographic algorithms.

Fabrizio De Santis studied computer engineering at Politecnico di Milano and completed his thesis at Advanced System Technology (AST) Laboratories (R&D) of STMicroelectronics. In 2011 he joined the Institute for Security in Information
Technology at Technische Universität München. In the period 2011 – 2013 he worked on the development of secure cryptographic implementations at Infineon Technologies AG in München.

Abstract:
The goal of the lecture is to present the latest achievements in Answer Set Programming (ASP). In particular, the focus of the lecture is on algorithms for solving optimization problems in ASP, that is, problems encoded by ASP programs with weak constraints. As usual in ASP, solutions of a problem instance are represented by its stable models, or answer sets. If the input program also comprises weak constraints, each of its stable model is associated with a cost determined by the unsatisfied weak constraints. Hence, weak constraints define a cost function, so that stable models of smaller cost are preferred.
The lecture overviews several algorithms for computing the most preferred, or optimal, stable models, and provides some details on core-guided algorithms, which proved to be effective on industrial instances of MaxSAT, the optimization variant of the satisfiability problem for propositional formulas. These algorithms work by iteratively checking satisfiability of a formula that is relaxed at each step by using the information provided by unsatisfiable cores, i.e., sets of weak constraints that cannot be jointly satisfied by any stable model of the input program.
The lecture is of the interest for both students visiting Logic Programming course as well as researchers of technical faculty working on declarative solving of hard problems.

Bio:
Dr. Mario Alviano received his master degree from University of Calabria in 2007 and his PhD in 2010 from the same university. Both works were distinguished by awards: the master thesis won the “Italian best thesis in Artificial Intelligence” a prize awarded by AI*IA, the Italian Association for Artificial Intelligence and PhD thesis was one among three dissertations awarded with a honorable mention by the European Coordinating Committee for Artificial Intelligence (ECCAI). Since 2011 he worked as a post doc and then as Assistant Professor at the Department of Mathematics and Computer Science, University of Calabria. The research interests of Dr. Alviano are spread throughout the field of knowledge representation and reasoning with the main focus on theoretical background and applications of answer set programming.

Abstract:

We present the novel concept of Controllable Face Privacy. Existing methods that alter face images to conceal identity inadvertently also destroy other facial attributes such as gender, race or age. This all-or-nothing approach is too harsh. Instead, we propose a flexible method that can independently control the amount of identity alteration while keeping unchanged other facial attributes. To achievethis flexibility, we apply a subspace decomposition onto our face encoding scheme, effectively decoupling facial attributes such as gender, race, age, and identity into mutually orthogonal subspaces, which in turn enables independent control of these attributes. Our method is thus useful for nuanced face de-identification, in which only facial identity is altered, but others, such gender, race and age, are retained. These altered face images protect identity privacy, and yet allow other computer vision analyses, such as gender detection, to proceed unimpeded. Controllable Face Privacy is therefore useful for reaping the benefits of surveillance cameras while preventing privacy abuse. Our proposal also permits privacy to be applied not just to identity, but also to other facial attributes as well. Furthermore, privacy-protection mechanisms, such as k-anonymity, L-diversity, and t-closeness, may be readily incorporated into our method. Extensive experiments with a commercial facial analysis software show that our alteration method is indeed effective.

Biography:

Dr. Terence Sim is an Associate Professor at the School of Computing, National University of Singapore. He teaches an undergraduate course in digital special effects, as well as a graduate course in multimedia. For research, Terence works primarily in these areas: face recognition, biometrics, and computational photography. He is also interested in computer vision problems in general, such as shape-from-shading, photometric stereo, object recognition. On the side, he dabble with some aspects of music processing, such as polyphonic music transcription. Terence also serves as President of the Pattern Recognition and Machine Intelligence Association (PREMIA), a national professional body for pattern recognition, affiliated with the International Association for Pattern Recognition (IAPR). Terence counts it a blessing and a joy to graduate from three great schools: Carnegie Mellon University, Stanford, and MIT.
Personal Website: https://www.comp.nus.edu.sg/~tsim/

In diesem Vortrag stellen wir unsere Beiträge zur Auswahl und Anwendung von Methoden der Informatik in Forschung und Lehre vor. Neben aktuellen Forschungsarbeiten im Bereich des computer-gestützten kollaborativen Lernens (CSCL) mittels Triangulation sich ergänzender Forschungsmethoden führen wir auch das fachdidaktische Konzept der Methodenausbildung an der TU Clausthal für den Übergang zwischen Bachelor- und Masterstudium ein. Dieses ist für den internationalen Charakter des Fachbereiches und die hohe Fluktuation zwischen beiden Studienabschnitten von hoher Bedeutung für die Qualität der Lehre.

Die Informatik ist ein wissenschaftliches Fachgebiet, in dem es in nur wenigen Jahrzehnten wesentliche von Schlüsselpersonen getragene Entwicklungen gab – und wohl auch weiterhin geben wird. Diese rasante Weiterentwicklung des Fachs Informatik sowie der Wandel des Erscheinungsbildes in der Öffentlichkeit legen nahe, dass sich deren Inhalte wie Prinzipien nicht einfach in unterrichtsspezifische Dosen abpacken lassen. Der Vortrag beleuchtet daher unterschiedliche Gebiete der Informatik und zeigt anhand von einigen konkreten Beispielen, wie Wissen und informatische Konzepte, die zunächst nur einigen wenigen Experten vorbehalten waren, alters- und entwicklungsstufengerecht Einzug in den Unterricht halten konnten und können.