Foundations of Security: Cryptography, Protocols, Trust
Joshua Guttman
Worcester Polytechnic University (USA)
Course site: http://web.cs.wpi.edu/~guttman/biss/
Abstract: Distributed or web-based systems raise challenging security problems,
because different participants may use them for conflicting goals. A
security infrastructure for a distributed system must provide
mechanisms for mutual authentication between participants; for
authorizing their actions; for delegating the right to act on behalf
of a principal; and for ensuring confidentiality.
This course will contain three main components. First, we will study
the foundations of cryptography. This consists of a collection of
definitions---based on games and on complexity for probabilistic
computations---and constructions to achieve them. Second, security
protocols, which use these cryptographic methods to achieve
authentication, confidentiality, and related properties, raise
challenges for analysis and design. Finally, we will consider access
control, especially as represented in logical trust management
theories.
Stochastic Process Algebras for Quantitative Analysis
Jane Hillston
University of Edinburgh (UK)
Course site: http://homepages.inf.ed.ac.uk/jeh/biss2013/
Abstract: Process algebras were originally conceived to provide models of the
functional behaviour of concurrent systems, allowing the modeller to
study the correctness properties of such systems. However, simple
additions to a process algebra can make it suitable also to support
the analysis of non-functional properties such
as reliability, availability and performance.
In this course we will consider stochastic process algebras in which
the algebra is enriched with activities which have an
associated stochastic duration. We will demonstrate how such a
language can be used for performance modelling of computer
systems and more general forms of analysis of dynamic behaviour.
Moreover, we will show how the compositional structure of the process
algebra can be used to help in the dynamic analysis. Being discrete
state models, stochastic process algebra models are prone to the
problem of state space explosion but recent results on fluid
semantics allow automated approximation of extremely large systems
using continuous state representations. This approach will also be
explained and demonstrated.
Throughout the course the concepts will be illustrated by examples.
Shape and Visual Apperance Acquisition for Photo-realistic Visualization
Fabio Ganovelli & Massimiliano Corsini
CNR (Italy)
Course site: http://vcg.isti.cnr.it/~ganovell/bertinoro
Program:
Prerequisites: Algebra, Advanced Calculus, Data Structure.
1. Algorithms and Data Structure for the Geometry Acquisition
Introduction. Range maps and Polygonal Meshes. Notes on cellular complexes: geometry and topology.
1.1 Geometry measurement
Triangulation. Time-of-flight. Image-based techniques.
1.2 Range maps alignment algorithms
Global alignment: Feature-based techniques. 4PCS algorithm. Optimization-on-a-manifold (OOM). Refinement: Iterative Closest Point (ICP).
1.3 Mesh reconstruction algorithms
Combinatorial techniques: zippering, ball pivoting, front advancing.
Volumetric techniques: Marching Cubes (MC), Poisson reconstruction, Moving Least Square (MLS).
1.4 Mesh complexity and simplification algorithms
Finding approximate representation that preserve geometry, topology and quality.
Vertex clusterization. Quadric-based mesh simplification.
2. Visual Appearance Acquisition
2.1 Introduction
Light-matter interaction. Radiometry in a nutshell. Bidirectional Reflectance Distribution Function (BRDF) and Bidirectional Surface Scattering Reflectance Distribution function (BSSRDF).
2.2 BRDF measurement
Gonioreflectometer. Image-based estimation. BRDF factorization through spherical harmonics. BRDF models. Fitting.
2.3 Reflectance as N-dimensional function estimation
Taxonomy. Reflectance Field. Bidirectional Texture Function (BTF). Surface Light Field. Reflection Transformation Imaging: polynomial (PTM) and hemispherical harmonics approximation (HSH).
2.5 Texture registration
Multi-modal matching through feature-based (keypoints, lines) and statistical methods (mutual information). Shading problems, intrinsic images and blending (in brief).