Open platform

Abstract: In this paper we describe the OpenGeoSys (OGS) project, which is a scientific open-source initiative for numerical simulation of thermo-hydro-mechanical-chemical processes in porous media. The basic concept is to provide a flexible numerical framework (using primarily the Finite Element Method (FEM)) for solving multifield problems in porous and fractured media for applications in geoscience and hydrology. To this purpose OGS is based on an object-oriented FEM concept including a broad spectrum of interfaces for pre- and postprocessing. The OGS idea has been in development since the mid-eighties. We provide a short historical note about the continuous process of concept and software development having evolved through Fortran, C, and C++ implementations. The idea behind OGS is to provide an open platform to the community, outfitted with professional software-engineering tools such as platform-independent compiling and automated benchmarking. A comprehensive benchmarking book has been prepared for publication. Benchmarking has been proven to be a valuable tool for cooperation between different developer teams, for example, for code comparison and validation purposes (DEVOVALEX and CO2 BENCH projects). On one hand, object-orientation (OO) provides a suitable framework for distributed code development; however, the parallelization of OO codes still lacks efficiency. High-performance-computing efficiency of OO codes is subject to future research.

Abstract: Publisher Summary Open source software, developed by volunteers, appears counter to the conventional wisdom that without ownership rights or government intervention, public goods will not be efficiently provided. But complexity makes a difference: contracts are incomplete and ownership rights do not necessarily elicit socially optimal effort. There are three mechanisms that improve the provision of complex software: pre-packaging, Application Program Interfaces, and Free/Open Source Software (F/OSS). F/OSS extends the range of products available to consumers, complementing, rather than replacing, proprietary provision. Pre-packaged software addresses common uses with limited feature sets, while firms with specialized, more complex needs use F/OSS. This chapter discusses sound economic reason why firms contribute to F/OSS development even when proprietary products are available from non-rival firms. This inquiry might reveal something about the limits of effective proprietary provision.

Abstract: A vehicle computer system has a housing sized to be mounted in a vehicle dashboard or other appropriate location. A computer is mounted within the housing and executes an open platform, multi-tasking operating system. The computer runs multiple applications on the operating system, including both vehicle-related applications (e.g., vehicle security application, vehicle diagnostics application, communications application, etc.) and non-vehicle-related applications (e.g., entertainment application, word processing, etc.). The computer system has an Internet wireless link to provide access to the Internet. One or more of the applications may utilize the link to access content on the Internet.

Abstract: Recent works have demonstrated that deep learning algorithms were efficient to conduct security evaluations of embedded systems and had many advantages compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies in some specific contexts. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in machine learning and (2) it does not allow for the reproducibility of the presented results and (3) it does not allow to draw general conclusions. This paper aims to address these limitations in several ways. First, completing recent works, we propose a study of deep learning algorithms when applied in the context of side-channel analysis and we discuss the links with the classical template attacks. Secondly, for the first time, we address the question of the choice of the hyper-parameters for the class convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a challenging masked implementation of the AES algorithm. Interestingly, our work shows that the approach followed to design the algorithm VGG-16 used for image recognition seems also to be sound when it comes to fix an architecture for side-channel analysis. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign of electromagnetic measurements exploited in our benchmarks. This open database, named ASCAD, is the first one in its category and it has been specified to serve as a common basis for further works on this subject.