773,751 research outputs found

    Event-B patterns and their tool support

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    Event-B has given developers the opportunity to construct models of complex systems that are correct-by-construction. However, there is no systematic approach, especially in terms of reuse, which could help with the construction of these models. We introduce the notion of design patterns within the framework of Event-B to shorten this gap. Our approach preserves the correctness of the models, which is critical in formal methods and also reduces the proving effort. Within our approach, an Event-B design pattern is just another model devoted to the formalisation of a typical sub-problem. As a result, we can use patterns to construct a model which can subsequently be used as a pattern to construct a larger model. We also present the interaction between developers and the tool support within the associated RODIN Platform of Event-B. The approach has been applied successfully to some medium-size industrial case studie

    Design of teacher assistance tools in an exploratory learning environment for algebraic generalisation

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    The MiGen project is designing and developing an intelligent exploratory environment to support 11-14 year-old students in their learning of algebraic generalisation. Deployed within the classroom, the system also provides tools to assist teachers in monitoring students' activities and progress. This paper describes the architectural design of these Teacher Assistance tools and gives a detailed description of one such tool, focussing in particular on the research challenges faced, and the technologies and approaches chosen to implement the necessary functionalities given the context of the project

    Feasibility of EPC to BPEL Model Transformations Based on Ontology and Patterns

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    Model-Driven Engineering holds the promise of transforming\ud business models into code automatically. This requires the concept of\ud model transformation. In this paper, we assess the feasibility of model\ud transformations from Event-driven Process Chain models to Business\ud Process Execution Language specifications. To this purpose, we use a\ud framework based on ontological analysis and workflow patterns in order\ud to predict the possibilities/limitations of such a model transformation.\ud The framework is validated by evaluating the transformation of several\ud models, including a real-life case.\ud The framework indicates several limitations for transformation. Eleven\ud guidelines and an approach to apply them provide methodological support\ud to improve the feasibility of model transformation from EPC to\ud BPEL

    Language and tool support for event refinement structures in Event-B

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    Event-B is a formal method for modelling and verifying the consistency of chains of model refinements. The event refinement structure (ERS) approach augments Event-B with a graphical notation which is capable of explicit representation of control flows and refinement relationships. In previous work, the ERS approach has been evaluated manually in the development of two large case studies, a multimedia protocol and a spacecraft sub-system. The evaluation results helped us to extend the ERS constructors, to develop a systematic definition of ERS, and to develop a tool supporting ERS. We propose the ERS language which systematically defines the semantics of the ERS graphical notation including the constructors. The ERS tool supports automatic construction of the Event-B models in terms of control flows and refinement relationships. In this paper we outline the systematic definition of ERS including the presentation of constructors, the tool that supports it and evaluate the contribution that ERS and its tool make. Also we present how the systematic definition of ERS and the corresponding tool can ensure a consistent encoding of the ERS diagrams in the Event-B models

    Clear Visual Separation of Temporal Event Sequences

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    Extracting and visualizing informative insights from temporal event sequences becomes increasingly difficult when data volume and variety increase. Besides dealing with high event type cardinality and many distinct sequences, it can be difficult to tell whether it is appropriate to combine multiple events into one or utilize additional information about event attributes. Existing approaches often make use of frequent sequential patterns extracted from the dataset, however, these patterns are limited in terms of interpretability and utility. In addition, it is difficult to assess the role of absolute and relative time when using pattern mining techniques. In this paper, we present methods that addresses these challenges by automatically learning composite events which enables better aggregation of multiple event sequences. By leveraging event sequence outcomes, we present appropriate linked visualizations that allow domain experts to identify critical flows, to assess validity and to understand the role of time. Furthermore, we explore information gain and visual complexity metrics to identify the most relevant visual patterns. We compare composite event learning with two approaches for extracting event patterns using real world company event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data Science (VDS), 201

    Interactive Visual Analysis of Networked Systems: Workflows for Two Industrial Domains

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    We report on a first study of interactive visual analysis of networked systems. Working with ABB Corporate Research and Ericsson Research, we have created workflows which demonstrate the potential of visualization in the domains of industrial automation and telecommunications. By a workflow in this context, we mean a sequence of visualizations and the actions for generating them. Visualizations can be any images that represent properties of the data sets analyzed, and actions typically either change the selection of data visualized or change the visualization by choice of technique or change of parameters

    Social network analysis shows direct evidence for social transmission of tool use in wild chimpanzees

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    The authors are grateful to the Royal Zoological Society of Scotland for providing core funding for the Budongo Conservation Field Station. The fieldwork of CH was funded by the Leverhulme Trust, the Lucie Burgers Stichting, and the British Academy. TP was funded by the Canadian Research Chair in Continental Ecosystem Ecology, and received computational support from the Theoretical Ecosystem Ecology group at UQAR. The research leading to these results has received funding from the People Programme (Marie Curie Actions) and from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013) REA grant agreement n°329197 awarded to TG, ERC grant agreement n° 283871 awarded to KZ. WH was funded by a BBSRC grant (BB/I007997/1).Social network analysis methods have made it possible to test whether novel behaviors in animals spread through individual or social learning. To date, however, social network analysis of wild populations has been limited to static models that cannot precisely reflect the dynamics of learning, for instance, the impact of multiple observations across time. Here, we present a novel dynamic version of network analysis that is capable of capturing temporal aspects of acquisition-that is, how successive observations by an individual influence its acquisition of the novel behavior. We apply this model to studying the spread of two novel tool-use variants, "moss-sponging'' and "leaf-sponge re-use,'' in the Sonso chimpanzee community of Budongo Forest, Uganda. Chimpanzees are widely considered the most "cultural'' of all animal species, with 39 behaviors suspected as socially acquired, most of them in the domain of tool-use. The cultural hypothesis is supported by experimental data from captive chimpanzees and a range of observational data. However, for wild groups, there is still no direct experimental evidence for social learning, nor has there been any direct observation of social diffusion of behavioral innovations. Here, we tested both a static and a dynamic network model and found strong evidence that diffusion patterns of moss-sponging, but not leaf-sponge re-use, were significantly better explained by social than individual learning. The most conservative estimate of social transmission accounted for 85% of observed events, with an estimated 15-fold increase in learning rate for each time a novice observed an informed individual moss-sponging. We conclude that group-specific behavioral variants in wild chimpanzees can be socially learned, adding to the evidence that this prerequisite for culture originated in a common ancestor of great apes and humans, long before the advent of modern humans.Publisher PDFPeer reviewe
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