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Abstract: Testing is an essential process for ensuring the quality of the software. Designing software with as few errors as possible in most embedded systems is often critical. Resource usage is a significant concern for proper behaviour because of the very nature of embedded systems. To design energy-efficient systems, approaches are needed to catch desirable consumption points and correct them before dep...
Abstract: Autonomous systems are supposed to automatically plan their actions and execute the plan without human intervention. In this paper, we propose a model-based two-layer frame-work for policy synthesis and test-case generation for autonomous systems. At the high-level layer of the framework, we have two kinds of methods for synthesising policies whose correctness is guaranteed by model checking. The ...
Abstract: Many industrial application domains utilize safety-critical systems to implement Programmable Logic Controllers (PLCs) software. These systems typically require a high degree of testing and stringent coverage measurements that can be supported by state-of-the-art automated test generation techniques. However, their limited application to PLCs and corresponding development environments can impact t...
Abstract: While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical applications is expanding, emerging DNNs experience massive growth in terms of computation power. It raises the necessity of improving the reliability of DNN accelerators yet reducing the computational burden on the hardware platforms, i.e. reducing the energy consumption and execution time as well as increasing the e...
Abstract: Convolutional Neural Networks (CNNs) have received great attention in the computer vision domain. However, CNNs are vulnerable to adversarial attacks, which are manipulations of input data that are imperceptible to humans but can fool the network. Several studies tried to address this issue, which can be divided into two categories: (i) training the network with adversarial examples, and (ii) opti...
Abstract: Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, requires large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotiv...
Abstract: Modeling is an essential and challenging activity in any engineering environment. It implies some hard-to-train skills such as abstraction and communication. Teachers, project leaders, and tool vendors have a hard time teaching or training their students, co-workers, or users. Gamification refers to the exploitation of gaming mechanisms for serious purposes, like promoting behavioral changes, soli...
Abstract: Visual Analytics (VA) is a multidisciplinary field that requires various skills including but not limited to data analytics, visualizations, and the corresponding domain knowledge. Recently, many studies proposed creating and using Domain-Specific Languages (DSLs) for VA in order to abstract complexities and assist designers in developing better VAs for different data domains. However, development...
Abstract: It is a great pleasure for the editorial committee to introduce the proceedings of the 2023 2nd International Conference on New Energy Technology Innovation and Low-carbon Development (NET-LC 2023). The conference took place in Changsha, China during January 6th-8th, 2023 (hybrid conference), with the attendance of about 50 scholars, experts, scientists and researchers in relevant domains. NET-LC ...
Abstract: Interacting with a monolithic architecture model to describe the architecture of large-scale software-intensive systems can be a complex and daunting task. The plethora of various concerns being addressed in a single model can impede the ability of individual stakeholders to discern their aspects of relevance. Architectural views allow to spread the various concerns into multiple (smaller) models,...
Abstract: Engineers commonly use informal diagrams for sketching, brainstorming, and communicating initial system designs. Diagramming is accessible, new concepts can be added freely, and diagrams can be specifically adjusted to communicate at the exact right level of abstraction depending on the audience. However, the information carried by informal diagrams is most often not precise enough for automation ...
Abstract: Blended modelling is an emerging trend in Model-Driven Engineering for complex software architectures. It enables the modelling of diverse architectural aspects through multiple editing notations seamlessly, interchangeably, and collaboratively. Blended modelling is expected to significantly improve productivity and user experience for multiple stakeholders. To manually architect and build a blend...
Abstract: Deep reinforcement learning has made significant development in recent years, and it is currently applied not only in simulators and games but also in embedded systems. However, when implemented in a real-world context, reinforcement learning is frequently shown to be unstable and incapable of adapting to realistic situations, particularly when directing a large number of agents. In this paper, we...
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