Unvoreingenommen meint jeder zu wissen, worum es sich beim Zufall handelt: Etwas, das man nicht prognostizieren kann: ob ich eine gefährliche Operation überleben werde, ob ich beim nächsten Lotto den Haupttreffer landen werde – Dinge dieser Art.

Eigenartigerweise hat der Begriff des Zufalls sogar in exakten Wissenschaften einen fest verankerten Platz erhalten. So zum Beispiel in der Physik. Wenn man ein radioaktives Kohlenstoffatom vor sich hat, kann man auf keine nur denkbare Art und Weise berechnen oder durch Messung bestimmen, zu welcher Zeit es zerfallen wird. Es zerfällt plötzlich irgendwann, ohne dass dafür eine Ursache genannt werden kann – spontan und zufällig. Und in der Biologie hält sich seit Darwin das Paradigma, dass pur zufällige Mutationen die Triebkraft der Entwicklung der Arten nach dem Prinzip der „selection of the fittest“ darstellen.

Allerdings spricht der Naturwissenschafter das Wort Zufall gerne ohne langes Grübeln aus, und es ist auch nicht ganz einfach, eine schlüssige Definition von „Zufall“ anzugeben. Aus der Sicht der Mathematik kann man jedoch den Begriff „Zufall“ einigermaßen gut verstehen: er ist eine Erfindung des Menschen. Um welche Erfindung es sich genau handelt, wird im Vortrag beschrieben.

I describe the digital forensics course (one semester) that we teach to year 2 undergraduate students at Deakin University. Along with some details about the students and their response to the course, I outline the two practical assignments they do, and for the second of these use forensic tools to show how data can be hidden in and retrieved from files.

In many commercial applications of broadcast, it is desirable that only those users who have paid for the service can retrieve broadcast data. Typical examples of such applications are pay-TV and copyright-protected digital materials. To ensure that only those users who have paid for the service can access materials, encryption of the broadcast is implemented. While only privileged users have secret keys to decrypt broadcast programs, malicious users may leak the secret keys to non-privileged users. In order to overcome this problem, secret keys are often kept in tamper-proof smart cards. When a user registers for the system, the service provider issues a smart card which is used for decryption of broadcast programs. In any subscriber scenario, the set of privileged users is dynamically changing and users may unsubscribe from the service at any time. A challenging problem is how to stop them to continue to obtain the broadcast data. This is called the revocation problem.

Although smart cards are intended to be tamper-proof, it may be possible to compromise them. With secret keys extracted from a compromised smart card, a pirate may create many clones of the smart card and sell them. Thus, an additional challenge is how to trace the source of a captured illegally cloned smart card and revoke all cloned cards. This is called the traitor tracing problem. In this work, our goal is to develop an efficient broadcast key distribution scheme, with revocation, which allows only privileged users to get access to the broadcast key. We aim to achieve this efficiently by using smart cards with limited computing power, communication capability, and storage. We assume the existence of a group controller (GC), which plays the role of a trusted third party. The GC broadcasts the encrypted data and key information through separate channels. The broadcast channels are insecure. Each privileged user is equipped with a Set Top Terminal (STT) with no return channel. In general, the STT is composed of a communication device, a tamper-proof decoder and a smart card slot in which a tamper-proof smart card (SC) is placed for key management. We implement polynomials over finite fields to establish efficient broadcast key distribution and efficient revocation.

(Joint paper of Prof. Lynn Batten (Deakin University) and co-author Dr. Xun Yi (Victoria University, Melbourne))

Research in vision science has an intrinsic potential for integrating contributions from psychology, computer science, engineering, optics, neuroscience, and physiology, among many other areas of knowledge. During the past 15 years, many vision researchers have successfully demonstrated that the results of such interdisciplinary efforts can advance the state of the art and lead to promising discoveries.

This talk presents representative research results that blend experiments in human visual perception and computer vision models to solve challenging vision problems. Particularly, it discusses the issues of object and scene recognition and the role of context and shows how they are being addressed by the leading researchers in the field.

After introducing selected basic concepts of object detection and recognition, scene recognition and analysis, and the role of context, we will discuss representative attempts to model the process of context influences in object perception. We will then motivate further research efforts by presenting a number of fascinating open problems in this field and suggesting how they can be approached in a truly interdisciplinary way.

Dr. Oge Marques is an Associate Professor in the Department of Computer Science and Engineering at Florida Atlantic University in Boca Raton, Florida. He is currently a guest professor with ITEC at University of Klagenfurt. He received his Ph.D. in Computer Engineering from Florida Atlantic University in 2001, his Masters in Electronics Engineering from Philips International Institute (Eindhoven, NL) in 1989 and his Bachelor’s Degree in Electrical Engineering from UTFPR (Curitiba, Brazil), where he also taught for more than 10 years before moving to the USA.

His research interests include: image processing, analysis, annotation, search, and retrieval; human and computer vision; and video processing and analysis. He has more than 20 years of teaching and research experience in the fields of image processing and computer vision, in different countries (USA, Austria, Brazil, Netherlands, Spain, and India) and capacities. He is the (co-) author of 4 (four) books in these topics, including the forthcoming textbook “Practical Image and Video Processing Using MATLAB” (Wiley, 2010). He has also

Wir stellen eine Architektur vor, die als Experimentierfeld zum Programmieren von Systemen mit mehreren Prozessoren gedacht ist. Deren Implementation besteht aus 12 identischen Prozessoren mit lokalen Speichern, die durch ein Netzwerk verbunden sind. Dazu wird ein einziger, programmierbarer Baustein (FPGA) verwendet.

Wir stellen eine Architektur vor, die als Experimentierfeld zum Programmieren von Systemen mit mehreren Prozessoren gedacht ist. Deren Implementation besteht aus 12 identischen Prozessoren mit lokalen Speichern, die durch ein Netzwerk verbunden sind. Dazu wird ein einziger, programmierbarer Baustein (FPGA) verwendet.