Assurance Cases for Proofs as Evidence

Proof-carrying code (PCC) provides a 'gold standard' for establishing formal and objective confidence in program behavior. However, in order to extend the benefits of PCC - and other formal certification techniques - to realistic systems, we must establish the correspondence of a mathematical proof of a program's semantics and its actual behavior. In this paper, we argue that assurance cases are an effective means of establishing such a correspondence. To this end, we present an assurance case pattern for arguing that a proof is free from various proof hazards. We also instantiate this pattern for a proof-based mechanism to provide evidence about a generic medical device software

Node localization for indoor tracking using artificial neural network

Wireless sensor network (WSN) always comes up with the need of deploying either mobile or immobile sensor nodes or both. Wireless communication among these nodes is crucial and it requires identifying the location of these nodes within a specific region. Global positioning system (GPS) is widely used for location tracking. However, when it comes to WSN, GPS has its limitations, due to its high power consumption and the overhead of additional hardware cost. The research challenge here lies in the efficient location tracking of wireless sensor nodes, especially in closed indoor and outdoor environments. This paper comes up with a simple and easy-to-implement technique using artificial neural networks (ANNs) to manipulate the location of the sensor nodes. In this paper, the back-propagation network training algorithm for providing supervised learning to multilayer perceptron is generalized to synthesize the WSN and gives out 2D Cartesian coordinates of the nodes. The technique is both cost-efficient and achieves 98% accuracy

Can Administration of Potentized Homeopathic Remedy, Arsenicum Album, Alter Antinuclear Antibody (ANA) Titer in People Living in High-Risk Arsenic Contaminated Areas? I. A Correlation with Certain Hematological Parameters

To examine whether elevated antinuclear antibody (ANA) titers reported in random human population of arsenic contaminated villages can be reverted to the normal range by administration of a potentized homeopathic drug, Arsenicum album, randomly selected volunteers in two arsenic contaminated villages and one arsenic-free village in West Bengal (India) were periodically tested for their ANA titer as well as various blood parameters in two types of experiments: ‘placebo-controlled double blind’ experiment for shorter duration and ‘uncontrolled verum fed experiment’ for longer duration. Positive modulation of ANA titer was observed along with changes in certain relevant hematological parameters, namely total count of red blood cells and white blood cells, packed cell volume, hemoglobin content, erythrocyte sedimentation rate and blood sugar level, mostly within 2 months of drug administration. Thus, Arsenicum album appears to have great potential for ameliorating arsenic induced elevated ANA titer and other hematological toxicities

ABSTRACT Explaining Abstract Counterexamples

When a program violates its specification a model checker produces a counterexample that shows an example of undesirable behavior. It is up to the user to understand the error, locate it, and fix the problem. Previous work introduced a technique for explaining and localizing errors based on finding the closest execution to a counterexample, with respect to a distance metric. That approach was applied only to concrete executions of programs. This paper extends and generalizes the approach by combining it with predicate abstraction. Using an abstract state-space increases scalability and makes explanations more informative. Differences between executions are presented in terms of predicates derived from the specification and program, rather than specific changes to variable values. Reasoning to the cause of an error from the fact that in the failing run x < y, but in the successful execution x = y is easier than reasoning from the information that in the failing run y = 239, but in the successful execution y = 232. An abstract explanation is automatically generalized. Predicate abstraction has previously been used in model checking purely as a state-space reduction technique. However, an abstraction good enough to enable a model checking tool to find an error is also likely to be useful as an automatically generated high-level description of a state space — suitable for use by programmers. Results demonstrating the effectiveness of abstract explanations support this claim