Armadillo 1.1: An Original Workflow Platform for Designing and Conducting Phylogenetic Analysis and Simulations

In this paper we introduce Armadillo v1.1, a novel workflow platform dedicated to designing and conducting phylogenetic studies, including comprehensive simulations. A number of important phylogenetic and general bioinformatics tools have been included in the first software release. As Armadillo is an open-source project, it allows scientists to develop their own modules as well as to integrate existing computer applications. Using our workflow platform, different complex phylogenetic tasks can be modeled and presented in a single workflow without any prior knowledge of programming techniques. The first version of Armadillo was successfully used by professors of bioinformatics at Université du Quebec à Montreal during graduate computational biology courses taught in 2010–11. The program and its source code are freely available at: <http://www.bioinfo.uqam.ca/armadillo>

Enhancing teamwork performance in mobile cloud-based learning

Mobile cloud-based learning is a novel trend that allows collaborative learning to happen among distributed learners, but it still lacks of mechanisms to enhance teamwork performance. Combining the features of the cloud, we have identified a learning flow based on Kolb team learning experience, executed by cloud-hosting learning management systems in conjunction with our newly designed system, \u27Teamwork as a Service (TaaS)\u27. Each of TaaS\u27s five web services aims to organize a certain type of learning activities, providing learners with an introduction, a \u27jigsaw classroom\u27, schedule planning, and mutual supervision during the whole collaborative learning process. In particular, enabling a rational group mechanism realized by the simulated annealing method, TaaS is able to allocate learners to their appropriate tasks in order to give their best performance. We also introduce details of the implementation of TaaS over the Amazon cloud

Back-to-Back Fault Injection Testing in Model-Based Development

Today, embedded systems across industrial domains (e.g., avionics, automotive) are representatives of software-intensive systems with increasing reliance on software and growing complexity. It has become critically important to verify software in a time, resource and cost effective manner. Furthermore, industrial domains are striving to comply with the requirements of relevant safety standards. This paper proposes a novel workflow along with tool support to evaluate robustness of software in model-based development environment, assuming different abstraction levels of representing software. We then show the effectiveness of our technique, on a brake-by-wire application, by performing back-to-back fault injection testing between two different abstraction levels using MODIFI for the Simulink model and GOOFI-2 for the generated code running on the target microcontroller. Our proposed method and tool support facilitates not only verifying software during early phases of the development lifecycle but also fulfilling back-to-back testing requirements of ISO 26262 when using model-based development

Cryptogamie / Algologie

Mobile cloud-based learning is a novel trend that allows collaborative learning to happen among distributed learners, but it still lacks of mechanisms to enhance teamwork performance. Combining the features of the cloud, we have identified a learning flow based on Kolb team learning experience, executed by cloud-hosting learning management systems in conjunction with our newly designed system, \u27Teamwork as a Service (TaaS)\u27. Each of TaaS\u27s five web services aims to organize a certain type of learning activities, providing learners with an introduction, a \u27jigsaw classroom\u27, schedule planning, and mutual supervision during the whole collaborative learning process. In particular, enabling a rational group mechanism realized by the simulated annealing method, TaaS is able to allocate learners to their appropriate tasks in order to give their best performance. We also introduce details of the implementation of TaaS over the Amazon cloud