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.

Prof. Fabio Stella | 11.11.2015 | 10:00 Uhr | E.1.42

Abstract

Non stationary continuous time Bayesian networks are presented and described. They allow to model systems where conditional independence relationships are allowed to change over time at discrete points in time. They build on the main blocks of continuous time Bayesian networks and non stationary dynamic Bayesian networks. The seminar presents the problem of non-stationary structural learning for such probabilistic graphical models and describes solution algorithms for three different settings. Furthermore, we present preliminary results of non stationary structural learning of Continuous Time Bayesian Networks on the following biological datasets; drosophila saccharomyces cerevisiae and songbird.

Fabio Stella is an associate professor at the Dipartimento di Informatica, Sistemistica e Comunicazione of the Università degli Studi di Milano-Bicocca. His research focuses on models and algorithms for data analysis and decision making under uncertainty in the areas of Business Intelligence, Data and Text Mining and Computational Finance. In the winter term 2015/16 he is giving the course 625.605 – Business Intelligence in Klagenfurt for the second time.

Prof. Tadeusz Morzy | 23.10.2015 | 14:00 Uhr | E.2.69

Abstract

Many applications require processing and analyzing sequential data. Examples include the analysis of passenger traveling histories, stock market prices, purchases of customers over time, meteorological events, workflow and RFID logs, etc. Recently, issues related to warehousing and analytical processing (OLAP) of sequential data have received growing attention. Particularly, the concept of Sequence OLAP (SOLAP) has been proposed that support OLAP processing of different kinds of aggregate queries on sequential data. The main feature distinguishing SOLAP from traditional OLAP is that data sequences managed by an SOLAP system are characterized by subsequence patterns they possess. The SOLAP systems allow to group data sequences based on patterns they possess and apply aggregate functions to each group. This kind of SOLAP queries are called a sequential pattern-based aggregate (PBA) queries. The processing of PBA queries is expensive due to the fact that they require full scan of all stored sequences. The natural question is how to efficiently evaluate this kind of queries?

The talk focuses on the new index structure supporting processing of sequential pattern-based aggregate queries. The structure of the index will be presented as well as classes of pattern-based aggregate queries supported by the index will be discussed. Finally, the performance of PBA queries using the proposed index will be presented.

Tadeusz Morzy is a professor in the Computing Science Department of Poznan University of Technology. He received his M. Sc., Ph. D. and Polish Habilitation from the Technical University of Poznań, Poland. He has held visiting positions at the Loyola University, New Orleans, Klagenfurt University Austria, University La Sapienza Italy, and Free University Amsterdam. He has authored and coauthored over 100 papers on databases, data mining, and data warehousing. He is co-author of a book on „Concurrency Control in Distributed Database Systems“ by North-Holland, editor and coauthor of “Handbook on Data Management” by Springer, and author of “Data Mining: Methods and Algorithms” (in Polish). He served as General Chair of the 2nd and 16th ADBIS Conferences (1998, 2012), and has served/serves on numerous program committees of international conferences and workshops. His research interests include data mining, data warehousing, transaction processing in database and data warehouse systems, access methods and query processing for databases, database optimization and performance evaluation.

Der Rückblick zum TEWI-Kolloquium von Laurens Rook am 17.7.2015 beinhaltet die Folien:

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.

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

Mustafa Alkhazraji | Thursday, 24th September 2015 | 11.00 am | B04, L4101

Abstract: The tremendous growth of mass applications such as digital cameras, Liquid-Crystal displays (LCD), Light Emission Diode (LED) displays and smart phones is due to the low cost of the integrated sensor manufacturing. Color sensors like CMOS photosensors are used as the main part of different applications. In this research, a fully integrated photo sensor in standard CMOS technology is presented, which enables light spectral analysis in the visible light range without additional optional component such as color filters. A new CMOS photo sensor has been proposed as a low cost alternative solution for the color sensing applications. The aim of this research is to investigate and implement integrated Digital Signal Processor (DSP) device as a complementary part of the proposed CMOS color sensor. The DSP has been designed to reconstruct and match the non-ideal output signals from the new CMOS color sensor structure with the tristimulus values X, Y and Z (color standards) by using linear transformation. The implemented design has been analyzed and verified at each stage of the work, using MATLAB, Modelsim from MentoGraphics, Design Compiler from Synopsys and Velocity/Encounter from Cadence Design Systems. In the end, the final layout of the design has been presented. Finally, due to the implementation of the DSP, a high number of sensor output signals enable the high accuracy of the colorimetric measurement by using pseudo-inverse matrix. Furthermore, the number of the word length has been used to represent the photosensor output signals and the linear transformation matrix. The latter is directly proportional to the measured tristimulus values X, Y and Z as well as the power and the silicon-chip area.

Keywords: Color sensor, CMOS technology, Digital Signal Processor, Integrated sensor, Color standards.