Peopleware, which includes anything related to the role of people in software Development (SD), has been arousing an increasing interest from boththe software industry and research community. this interest is due to ...
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To foster epistemic agency for visually impaired (VI) learners in STEM education for knowledge Building (KB), educational support that suits diverse image schemas may be necessary. By better understanding diverse imag...
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Unmanned aerial vehicles (UAVs) are increasingly applied in various mission scenarios due to their versatility, scalability and cost-effectiveness. In practical UAV application scenarios, UAV mission planning systems ...
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We propose a tumor growth model that can be parallelized using a classical master/slave design pattern on CUDA/GPU. the probabilistic algorithm on which the model presented here is based has shown excellent performanc...
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Identifying confidentiality violations is challenging as modern software-intensive systems exchange and store large amounts of data, and system deployment and context vary. Although model-based analyses can identify s...
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ISBN:
(纸本)9798400706226
Identifying confidentiality violations is challenging as modern software-intensive systems exchange and store large amounts of data, and system deployment and context vary. Although model-based analyses can identify such violations already at design time, uncertainty within a software system or its environment can void analysis results. Existing approaches to raising awareness of uncertainty sources are limited in usability and extendability and require expert knowledge for interpretation and analysis. this paper presents our collaborative tooling ARC(3)N for collecting, modeling, and analyzing uncertainty sources regarding confidentiality. Using an open web-based platform, we simplify both identifying and assessing uncertainty without requiring expert knowledge. We evaluate our approach with a user study with students, researchers, and practitioners (n = 17) and demonstrate its feasibility.
the primary focus of the recruitment division in every company is to fill vacant positions as quickly as possible. However, they fail to recognize the long-term consequences of this approach. On one hand, the staffing...
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the success of software development projects is significantly impacted by various risk factors, both predictable and unpredictable. this literature review aims to explore and evaluate innovative risk assessment and ma...
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the proceedings contain 156 papers. the topics discussed include: space system modeling approach for exploring of the iced-covered Ganymede moon subsurface oceans;M2AR: a web-based modeling environment for the augment...
ISBN:
(纸本)9798400706226
the proceedings contain 156 papers. the topics discussed include: space system modeling approach for exploring of the iced-covered Ganymede moon subsurface oceans;M2AR: a web-based modeling environment for the augmented reality workflow 1–5 modeling language;Nautilus: implementation of an evolution approach for graph databases;Cross-IDE remote debugging of model management programs through the debug adapter protocol;enhancing model management with automated REST API generation;keeping clients' models up-to-date with Edelta;IMP-Logics: a metamodel for analysis and transformations of datalog programs;modelling tool extension for vulnerability management;concretize: a model-driven tool for scenario-based autonomous vehicle testing;and CertGraph: towards a comprehensive knowledge graph for cloud security certifications.
Driven by the opportunities for condition monitoring and new business models, Digital Twins have become a popular topic within research and industrial application over the last years. therefore, universities must inte...
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Mission-critical embedded software is critical to our society's infrastructure but can be subject to new security vulnerabilities as technology advances. When security issues arise, Reverse Engineers (REs) use Sof...
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ISBN:
(纸本)9798350300376
Mission-critical embedded software is critical to our society's infrastructure but can be subject to new security vulnerabilities as technology advances. When security issues arise, Reverse Engineers (REs) use software Reverse engineering (SRE) tools to analyze vulnerable binaries. However, existing tools have limited support, and REs undergo a time-consuming, costly, and error-prone process that requires experience and expertise to understand the behaviors of software and vulnerabilities. To improve these tools, we propose cfg2vec, a Hierarchical Graph Neural Network (GNN) based approach. To represent binary, we propose a novel Graph-of-Graph (GoG) representation, combining the information of control-flow and function-call graphs. Our cfg2vec learns how to represent each binary function compiled from various CPU architectures, utilizing hierarchical GNN and the siamese network-based supervised learning architecture. We evaluate cfg2vec's capability of predicting function names from stripped binaries. Our results show that cfg2vec outperforms the state-of-the-art by 24.54% in predicting function names and can even achieve 51.84% better given more training data. Additionally, cfg2vec consistently outperforms the state-of-the-art for all CPU architectures, while the baseline requires multiple training to achieve similar performance. More importantly, our results demonstrate that our cfg2vec could tackle binaries built from unseen CPU architectures, thus indicating that our approach can generalize the learned knowledge. Lastly, we demonstrate its practicability by implementing it as a Ghidra plugin used during resolving DARPA Assured MicroPatching (AMP) challenges.
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