Two conflicting high-level goals govern the enforcement of security policies, abridged in the phrase ``high security at a low cost''. While these drivers seem irreconcilable, formal modelling languages and automated verification techniques can facilitate the task of finding the right balance. We propose a modelling language and a framework in which security checks can be relaxed or strengthened to save resources or increase protection, on the basis of trust relationships among communicating parties.

In some important biological phenomena Volterra integral and integrodifferential equations represent an appropriate mathematical model for the description of the dynamics involved (see e.g. [1], and the bibliography therein).

Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run.

This paper describes the efforts, pitfalls, and successes of applying unsupervised classification techniques to analyze the Trap-2017 dataset. Guided by the informative perspective on the nature of the dataset obtained through a set of specifically-written perl/bash scripts, we devised an automated clustering tool implemented in python upon openly-available scientific libraries. By applying our tool on the original raw data it is possibile to infer a set of trending behaviors for vehicles travelling over a route, yielding an instrument to classify both routes and plates.

In recent years we are witnessing a continuous growth in the amount of data that both public and private organizations collect and profit by. Search engines are the most common tools used to retrieve information, and more recently, clustering techniques showed to be an effective tool in helping users to skim query results.

The dynamic interaction of complex fluid interfaces is highly sensitive to near-contact interactions occurring at the scale of ten of nanometers. Such interactions are difficult to analyze because they couple self-consistently to the dynamic morphology of the evolving interface, as well as to the hydrodynamics of the interstitial fluid film.

Reputation systems are nowadays widely used to support decision making in networked systems. Parties in such systems rate each other and use shared ratings to compute reputation scores that drive their interactions. The existence of reputation systems with remarkable differences calls for formal approaches to their analysis. We present a verification methodology for reputation systems that is based on the use of the coordination language Klaim and related analysis tools.