Abstract: In this work we introduce a novel, flexible and robust traffic flow cellular automata model. Our proposal includes two important stages that make possible the consideration of different profiles of drivers' behaviors. We first consider the motion expectation of cars that are in front of each driver. Secondly, we define how a specific car decides to get around, considering the foreground traffic configuration. Our model uses stochastic rules for both situations, adjusting the Probability Density Function of the Beta Distribution for three neighborhoods drives behavior, adjusting different parameters of the Beta distribution for each one.

Abstract: Several 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.

Abstract: Computational reproducibility depends on being able to isolate necessary and sufficient computational artifacts and preserve them for later re-execution. Both isolation and preservation of artifacts can be challenging due to the complexity of existing software and systems and the resulting implicit dependencies, resource distribution, and shifting compatibility of systems as time progresses---all conspiring to break the reproducibility of an application. Sandboxing is a technique that has been used extensively in OS environments for isolation of computational artifacts. Several tools were proposed recently that employ sandboxing as a mechanism to ensure reproducibility. However, none of these tools preserve the sandboxed application for re-distribution to a larger scientific community---aspects that are equally crucial for ensuring reproducibility as sandboxing itself. In this paper, we describe a combined sandboxing and preservation framework, which is efficient, invariant and practical for large-scale reproducibility. We present case studies of complex high energy physics applications and show how the framework can be useful for sandboxing, preserving and distributing applications. We report on the completeness, performance, and efficiency of the framework, and suggest possible standardization approaches.

Abstract: The embedding of high-dimensional data into 2D (or 3D) space is the most popular way of data visualization. Despite recent advances in developing of very accurate dimensionality reduction algorithms, such as BH-SNE, Q-SNE and LoCH, their relatively high computational complexity still remains the obstacle for interactive visualization of truly large sets of high-dimensional data. We show that a new clone of the multidimensional scaling method (MDS) – nr-MDS – can be up to two orders of magnitude faster than the modern dimensionality reduction algorithms. We postulate its linear O(M) computational and memory complexity. Simultaneously, our method preserves in 2D and 3D target spaces high separability of data, similar to that obtained by the state-of-the-art dimensionality reduction algorithms. We present the effects of nr-MDS application in visualization of data repositories such as 20 Newsgroups (M=18000), MNIST (M=70000) and REUTERS (M=267000).

Abstract: Requirements engineering is crucial for software projects, but formal requirements engineering is often ignored in scientific software projects. Scientists do not often see the benefit of directing their time and effort towards documenting requirements. Additionally, there is a lack of requirements engineering knowledge amongst scientists who develop software. We aim at helping scientists to easily recover and reuse requirements without acquiring prior requirements engineering knowledge. We apply an automated approach to extract requirements for scientific software from available knowledge sources, such as user manuals and project reports. The approach employs natural language processing techniques to match defined patterns in input text. We have evaluated the approach in three different scientific domains, namely seismology, building performance and computational fluid dynamics. The evaluation results show that 78--97% of the extracted requirement candidates are correctly extracted as early requirements.