In this paper we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake, which causes additional detections. When the detections are fed to a tracking system like the probabilistic data association filter (PDAF), the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even ...

Collective Adaptive Systems (CAS) consist of a large number of interacting objects. The design of such systems requires scalable analysis tools and methods, which have necessarily to rely on some form of approximation of the system’s actual behaviour. Promising techniques are those based on mean-field approximation. The FlyFast model-checker uses an on-the-fly algorithm for bounded PCTL model-c...

In model checking, a counterexample is considered as a valuable tool for debugging. In Probabilistic Model Checking (PMC), counterexample generation has a quantitative aspect. The counterexample in PMC is a set of paths in which a path formula holds, and their accumulative probability mass violates the probability threshold. However, understanding the counterexample is not an easy task. In this...

Probabilisticmodel checking is a technique for verifyingwhether amodel such as aMarkov chain satisfies a probabilistic, behavioral property – e.g. ‘‘with probability at least 0.999, a device will be elected leader’’. Such properties are expressible in probabilistic temporal logics, e.g. PCTL, and efficient algorithms exist for checking whether these formulae are true or false on finite-state mo...

We review two novel methods for text categorization, based on a new framework that utilizes richer annotations that we call annotator rationales. A human annotator provides hints to a machine learner by highlighting contextual “rationales” in support of each of his or her annotations. We have collected such rationales, in the form of substrings, for an existing document sentiment classification...

Model checking probabilistic CTL properties of Markov decision processes with convex uncertainties has been recently investigated by Puggelli et al. Such model checking algorithms typically suffer from the state space explosion. In this paper, we address probabilistic bisimulation to reduce the size of such an MDP while preserving the probabilistic CTL properties it satisfies. In particular, we...

This paper is an introductory survey of available methods for the computation and representation of probabilistic counterexamples for discrete-time Markov chains and probabilistic automata. In contrast to traditional model checking, probabilistic counterexamples are sets of finite paths with a critical probability mass. Such counterexamples are not obtained as a by-product of model checking, bu...

In this paper, we give a probabilistic counterpart of  Mazur-Ulam theorem  in probabilistic normed groups. We show, under some conditions, that every surjective isometry between two probabilistic normed groups is a homomorphism.