PURRS: Towards Computer Algebra Support for Fully Automatic Worst-Case Complexity Analysis

Fully automatic worst-case complexity analysis has a number of applications in computer-assisted program manipulation. A classical and powerful approach to complexity analysis consists in formally deriving, from the program syntax, a set of constraints expressing bounds on the resources required by the program, which are then solved, possibly applying safe approximations. In several interesting cases, these constraints take the form of recurrence relations. While techniques for solving recurrences are known and implemented in several computer algebra systems, these do not completely fulfill the needs of fully automatic complexity analysis: they only deal with a somewhat restricted class of recurrence relations, or sometimes require user intervention, or they are restricted to the computation of exact solutions that are often so complex to be unmanageable, and thus useless in practice. In this paper we briefly describe PURRS, a system and software library aimed at providing all the computer algebra services needed by applications performing or exploiting the results of worst-case complexity analyses. The capabilities of the system are illustrated by means of examples derived from the analysis of programs written in a domain-specific functional programming language for real-time embedded systems.Comment: 6 page

Comparisons of Animal “Smarts” Using the First Four Stages of the Model of Hierarchical Complexity

The Model of Hierarchical Complexity is a behavioral model of development and evolution of the complexity of behavior. It is based on task analysis. Tasks are ordered in terms of their hierarchical complexity, which is an ordinal scale that measures difficulty. The hierarchical difficulty of tasks is categorized as the order of hierarchical complexity. Successful performance on a task is called the behavioral stage. This model can be applied to non-human animals, and humans. Using data from some of the simplest animals and also somewhat more complex ones, this analysis describes the four lowest behavioral stages and illustrate them using the behaviors of a range of simple organisms. For example, Stage 1 tasks, and performance on them, are addressed with automatic unconditioned responses. Behavior at this Stage includes sensing, tropisms, habituation and, other automatic behaviors. Single cell organisms operate at this Stage. Stage 2 tasks include these earlier behaviors, but also include respondent conditioning but not operant conditioning. Animals such as some simple invertebrates have shown respondent conditioning, but not operant conditioning. Stage 3 tasks coordinate three instances of these earlier tasks to make possible operant conditioning. These stage 3 performances are similar to those of some invertebrates and also insects. Stage 4 tasks organisms coordinate 2 or more circular sensory-motor task actions into a superordinate “concept”. This explanation of the early stages of the Model of Hierarchical Complexity may help future research in animal behavior, and comparative psychology.

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Stochastic Calculus of Wrapped Compartments

The Calculus of Wrapped Compartments (CWC) is a variant of the Calculus of Looping Sequences (CLS). While keeping the same expressiveness, CWC strongly simplifies the development of automatic tools for the analysis of biological systems. The main simplification consists in the removal of the sequencing operator, thus lightening the formal treatment of the patterns to be matched in a term (whose complexity in CLS is strongly affected by the variables matching in the sequences). We define a stochastic semantics for this new calculus. As an application we model the interaction between macrophages and apoptotic neutrophils and a mechanism of gene regulation in E.Coli

A Model Driven Approach to Water Resource Analysis based on Formal Methods and Model Transformation

AbstractSeveral frameworks have been proposed in literature in order to cope with critical infrastructure modelling issues, and almost all rely on simulation techniques. Anyway simulation is not enough for critical systems, where any problem may lead to consistent loss in money and even human lives. Formal methods are widely used in order to enact exhaustive analyses of these systems, but their complexity grows with system dimension and heterogeneity. In addition, experts in application domains could not be familiar with formal modelling techniques. A way to manage complexity of analysis is the use of Model Based Transformation techniques: analysts can express their models in the way they use to do and automatic algorithms translate original models into analysable ones, reducing analysis complexity in a completely transparent way.In this work we describe an automatic transformation algorithm generating hybrid automata for the analysis of a natural water supply system. We use real system located in the South of Italy as case study

Statistical keyword detection in literary corpora

Understanding the complexity of human language requires an appropriate analysis of the statistical distribution of words in texts. We consider the information retrieval problem of detecting and ranking the relevant words of a text by means of statistical information referring to the "spatial" use of the words. Shannon's entropy of information is used as a tool for automatic keyword extraction. By using The Origin of Species by Charles Darwin as a representative text sample, we show the performance of our detector and compare it with another proposals in the literature. The random shuffled text receives special attention as a tool for calibrating the ranking indices.Comment: Published version. 11 pages, 7 figures. SVJour for LaTeX2