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Abstract. We consider the problem of bounded model checking (BMC) for linear temporal logic (LTL). We present several efficient encodings that have size linear in the bound. Furthermore, we show how the encodings can be extended to LTL with past operators (PLTL). The generalised encoding is still of linear size, but cannot detect minimal length counterexamples. By using the virtual unrolling technique minimal length counterexamples can be captured, however, the size of the encoding is quadratic in the specification. We also extend virtual unrolling to Büchi automata, enabling them to accept minimal length counterexamples.Our BMC encodings can be made incremental in order to benefit from incremental SAT technology. With fairly small modifications the incremental encoding can be further enhanced with a termination check, allowing us to prove properties with BMC.An analysis of the liveness-to-safety transformation reveals many similarities to the BMC encodings in this paper. We conduct experiments to determine the advantage of employing dedicated BMC encodings for PLTL over combining more general but potentially less efficient approaches with BMC: the liveness-to-safety transformation with invariant checking and Büchi automata with fair cycle detection.Experiments clearly show that our new encodings improve performance of BMC considerably, particularly in the case of the incremental encoding, and that they are very competitive for finding bugs. Dedicated encodings seem to have an advantage over using more general methods with BMC. Using the liveness-to-safety translation with BDD-based invariant checking results in an efficient method to find shortest counterexamples that complements the BMC-based approach. For proving complex properties BDD-based methods still tend to perform better.

Summary: Hadoop-BAM is a novel library for the scalable manipulation of aligned next-generation sequencing data in the Hadoop distributed computing framework. It acts as an integration layer between analysis applications and BAM files that are processed using Hadoop. Hadoop-BAM solves the issues related to BAM data access by presenting a convenient API for implementing map and reduce functions that can directly operate on BAM records. It builds on top of the Picard SAM JDK, so tools that rely on the Picard API are expected to be easily convertible to support large-scale distributed processing. In this article we demonstrate the use of Hadoop-BAM by building a coverage summarizing tool for the Chipster genome browser. Our results show that Hadoop offers good scalability, and one should avoid moving data in and out of Hadoop between analysis steps.Availability: Available under the open-source MIT license at http://sourceforge.net/projects/hadoop-bam/Contact: matti.niemenmaa@aalto.fiSupplementary information: Supplementary material is available at Bioinformatics online.

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Internet of Things (IoT), the emerging computing infrastructure that refers to the networked interconnection of physical objects, incorporates a plethora of digital systems that are being developed by means of a large number of applications. Many of these applications administer data collection on the edge and offer data storage and analytics capabilities in the cloud. This raises the following problems: (i) the processing stages in IoT applications need to have separate implementations for both the edge and the cloud, (ii) the placement of computation is inflexible with separate software stacks, as the optimal deployment decisions need to be made at runtime, and (iii) unified fault tolerance is essential in case of intermittent long-distance network connectivity problems, malicious harming of edge devices, or harsh environments. This paper proposes a novel fault-tolerant architecture CEFIoT for IoT applications by adopting state-ofthe-art cloud technologies and deploying them also for edge computing. We solve the data fault tolerance issue by exploiting the Apache Kafka publish/subscribe platform as the unified high-performance data replication solution offering a common software stack for both the edge and the cloud. We also deploy Kubernetes for fault-tolerant management and the advanced functionality allowing on-the-fly automatic reconfiguration of the processing pipeline to handle both hardware and network connectivity based failures.

Abstract. Bounded model checking is an efficient method for finding bugs in system designs. The major drawback of the basic method is that it cannot prove properties, only disprove them. Recently, some progress has been made towards proving properties of LTL. We present an incremental and complete bounded model checking method for the full linear temporal logic with past (PLTL). Compared to previous works, our method both improves and extends current results in many ways: (i) our encoding is incremental, resulting in improvements in performance, (ii) we can prove non-existence of a counterexample at shallower depths in many cases, and (iii) we support full PLTL. We have implemented our method in the NuSMV2 model checker and report encouraging experimental results.

Abstract.We consider the problem of bounded model checking for linear temporal logic with past operators (PLTL). PLTL is more attractive as a specification language than linear temporal logic without past operators (LTL) since many specifications are easier to express in PLTL. Although PLTL is not more expressive than LTL, it is exponentially more succinct. Our contribution is a new more efficient encoding of the bounded model checking problem for PLTL based on our previously presented encoding for LTL. The new encoding is linear in the bound. We have implemented the encoding in the NuSMV 2.1 model checking tool and compare it against the encoding in NuSMV by Benedetti and Cimatti. The experimental results show that our encoding performs significantly better than this previously used encoding.

In this paper bounded model checking of asynchronous concurrent systems is introduced as a promising application area for answer set programming. As the model of asynchronous systems a generalisation of communicating automata, 1-safe Petri nets, are used. It is shown how a 1-safe Petri net and a requirement on the behaviour of the net can be translated into a logic program such that the bounded model checking problem for the net can be solved by computing stable models of the corresponding program. The use of the stable model semantics leads to compact encodings of bounded reachability and deadlock detection tasks as well as the more general problem of bounded model checking of linear temporal logic. Correctness proofs of the devised translations are given, and some experimental results using the translation and the Smodels system are presented.