Simulation is an important tool for the verification of modern complex time-critical communication systems, especially when worst-case schedulability analysis is not available. Evaluating worst-case traversal times via simulation traditionally involves resource-intensive long simulations that are poorly parallelizable. Recent research has demonstrated that aggregating many short simulations with randomized starting conditions yields substantial improvements over this classical approach in terms of likelihood of observing very large communication latencies. In this study, we explore the potential of many-objective optimization to further enhance the efficiency of the aggregation approach.To this end we further reduce the length of the aggregated simulations and perform many-objective optimization to set the starting conditions, namely the node start offsets and initial flow scheduling order. Our approach consists in modelling the approximation of worst-case traversal times as a many-objective Pareto optimization problem in the context of real-time Ethernet networks. Performance evaluation, conducted on different industrially relevant use cases from the automotive and aerospace domains, shows up to 46.42% increased end-to-end latencies for a 50 times shorter total simulation time, in comparison to the traditional approach of running single long simulations.

Recent successes in training word embeddings for Natural Language Processing ( NLP ) tasks have encouraged a wave of research on representation learning for source code, which builds on similar NLP methods. The overall objective is then to produce code embeddings that capture the maximum of program semantics. State-of-the-art approaches invariably rely on a syntactic representation (i.e., raw lexical tokens, abstract syntax trees, or intermediate representation tokens) to generate embeddings, which are criticized in the literature as non-robust or non-generalizable. In this work, we investigate a novel embedding approach based on the intuition that source code has visual patterns of semantics. We further use these patterns to address the outstanding challenge of identifying semantic code clones. We propose the WySiWiM ( ‘ ‘What You See Is What It Means ” ) approach where visual representations of source code are fed into powerful pre-trained image classification neural networks from the field of computer vision to benefit from the practical advantages of transfer learning. We evaluate the proposed embedding approach on the task of vulnerable code prediction in source code and on two variations of the task of semantic code clone identification: code clone detection (a binary classification problem), and code classification (a multi-classification problem). We show with experiments on the BigCloneBench (Java), Open Judge (C) that although simple, our WySiWiM approach performs as effectively as state-of-the-art approaches such as ASTNN or TBCNN. We also showed with data from NVD and SARD that WySiWiM representation can be used to learn a vulnerable code detector with reasonable performance (accuracy ∼90%). We further explore the influence of different steps in our approach, such as the choice of visual representations or the classification algorithm, to eventually discuss the promises and limitations of this research direction.

_knowscape is a digital data territory and an experimental project: an electronic space made of links, connections, relations, knowledge. Initially _knowscape has been conceived as an alternative multi-user browser using data *tracking* and *profiling* techniques to question, to reverse them, so to finally produce open data territories, shared browsing experiences and *open users' profiles*._knowscape evolved and has become since 2003 a mobile downloadable space, a variable space with no fixed or frozen size as well as no definite location: _knowscape mobile, a mobile information architecture that has always both a temporary location in the physical space and a world wide digital one over the Internet. Based on low esthetics and close to machines visual output, _knowscape mobile builds electronic spaces with information-based "voxels" [ 3D pixels ]. Each user or agent creates its own data architecture, made of contiguous voxels, the addition of these spaces creates a shared knowledge 3D territory that can be experienced by any other connected user._knowscape mobile relation to physical space is also simple and direct: boolean. In fact, it is the first architectural space that mixes data space with physical one through the use of boolean algebraic operations. In each installation, electronic devices open windows on this re-localized data territory, which allow visitors to interact either from the physical space or from the internet._knowscape mobile is thus an architectural space temporarily associating territory of data and physical space, linking architecture, knowledge and browsing._knowscape mobile :::: fabric | ch. http://knowscape.fabric.ch/mobile/.