An Introduction to Pervasive Interface Automata

Pervasive systems are often context-dependent, component based systems in which components expose interfaces and offer one or more services. These systems may evolve in unpredictable ways, often through component replacement. We present pervasive interface automata as a formalism for modelling components and their composition. Pervasive interface automata are based on the interface automata of Henzinger et al, with several significant differences. We expand their notion of input and output actions to combinations of input, output actions, and callable methods and method calls. Whereas interfaces automata have a refinement relation, we argue the crucial relation in pervasive systems is component replacement, which must include consideration of the services offered by a component and assumptions about the environment. We illustrate pervasive interface autmotata and component replacement with a small case study of a pervasive application for sports predictions

An Improvement of the Piggyback Algorithm for Parallel Model Checking

This paper extends the piggyback algorithm to enlarge the set of liveness properties it can verify. Its extension is motivated by an attempt to express in logic the counterexamples it can detect and relate them to bounded liveness. The original algorithm is based on parallel breadth-first search and piggybacking of accepting states that are deleted after counting a fixed number of transitions. The main improvement is obtained by renewing the counter of transitions when the same accepting states are visited in the negated property automaton. In addition, we describe piggybacking of multiple states in either sets (exact) or Bloom filters (lossy but conservative), and use of local searches that attempt to connect cycles fragmented among processing cores. Finally it is proved that accepting cycle detection is in NC in the size of the product automaton's entire state space, including unreachable states

BioDiVinE: A Framework for Parallel Analysis of Biological Models

In this paper a novel tool BioDiVinEfor parallel analysis of biological models is presented. The tool allows analysis of biological models specified in terms of a set of chemical reactions. Chemical reactions are transformed into a system of multi-affine differential equations. BioDiVinE employs techniques for finite discrete abstraction of the continuous state space. At that level, parallel analysis algorithms based on model checking are provided. In the paper, the key tool features are described and their application is demonstrated by means of a case study

Platform Dependent Verification: On Engineering Verification Tools for 21st Century

The paper overviews recent developments in platform-dependent explicit-state LTL model checking.Comment: In Proceedings PDMC 2011, arXiv:1111.006

Variations on Multi-Core Nested Depth-First Search

Recently, two new parallel algorithms for on-the-fly model checking of LTL properties were presented at the same conference: Automated Technology for Verification and Analysis, 2011. Both approaches extend Swarmed NDFS, which runs several sequential NDFS instances in parallel. While parallel random search already speeds up detection of bugs, the workers must share some global information in order to speed up full verification of correct models. The two algorithms differ considerably in the global information shared between workers, and in the way they synchronize. Here, we provide a thorough experimental comparison between the two algorithms, by measuring the runtime of their implementations on a multi-core machine. Both algorithms were implemented in the same framework of the model checker LTSmin, using similar optimizations, and have been subjected to the full BEEM model database. Because both algorithms have complementary advantages, we constructed an algorithm that combines both ideas. This combination clearly has an improved speedup. We also compare the results with the alternative parallel algorithm for accepting cycle detection OWCTY-MAP. Finally, we study a simple statistical model for input models that do contain accepting cycles. The goal is to distinguish the speedup due to parallel random search from the speedup that can be attributed to clever work sharing schemes.Comment: In Proceedings PDMC 2011, arXiv:1111.006

Conservation of freshwater bivalves at the global scale: diversity, threats and research needs

Bivalves are ubiquitous members of freshwater ecosystems and responsible for important functions and services. The present paper revises freshwater bivalve diversity, conservation status and threats at the global scale and discusses future research needs and management actions. The diversity patterns are uneven across the globe with hotspots in the interior basin in the United States of America (USA), Central America, Indian subcontinent and Southeast Asia. Freshwater bivalves are affected by multiple threats that vary across the globe; however, pollution and natural system (habitat) modifications being consistently found as the most impacting. Freshwater bivalves are among the most threatened groups in the world with 40% of the species being near threatened, threatened or extinct, and among them the order Unionida is the most endangered. We suggest that global cooperation between scientists, managers, politicians and general public, and application of new technologies (new generation sequencing and remote sensing, among others) will strengthen the quality of studies on the natural history and conservation of freshwater bivalves. Finally, we introduce the articles published in this special issue of Hydrobiologia under the scope of the Second International Meeting on Biology and Conservation of Freshwater Bivalves held in 2015 in Buffalo, New York, USA.This work was supported by FCT—Foundation for Science and Technology, Project 3599—Promote the Scientific Production and Technological Development and Thematic 3599-PPCDT by FEDER as part of the project FRESHCO: multiple implications of invasive species on Freshwater Mussel co-extinction processes (Contract: PTDC/AGRFOR/1627/2014). FCT also supported MLL under Grant (SFRH/BD/115728/2016)