查看更多>>摘要：Before anything else, I would like to apologize to the reader for the delay in the production and distribution of Ada Letters; although this being the December 2023 issue, it is arriving already a few months in 2024. This delay is in part due to the difficulties in the editing process (all volunteer), but also due to the potential changes that 2024 will bring to the Ada ecosystem of organizations and publications. These changes have also postponed the planned merger of Ada Letters with the sister publication, Ada User Journal, initially foreseen for the beginning of 2024. Please stay tuned for news on this process, during 2024.
查看更多>>摘要：[Messages without subject/newsgroups are replies from the same thread. Messages may have been edited for minor proofreading fixes. Quotations are trimmed where deemed too broad. Sender's signatures are omitted as a general rule. -arm]Dear Reader,One of the most significant features of Ada 2022 is the new light-weight parallelism. Although no Ada compiler implements it as of this writing, equivalent features are now available in library form thanks to Tucker Taft. Let us hope this brings us near an actual Ada 2022 implementation.
查看更多>>摘要：25th International Conference on Verification, Model Checking, and Abstract Interpretation(VMCAI'2024), London, UK. Co-located with POPL'2024. Topics include: program verification, model checking, abstract interpretation, static analysis, type systems, program certification, detection of bugs and security vulnerabilities, hybrid and cyber-physical systems, concurrent and distributed systems, analysis of numerical properties, analysis of smart contracts, etc., case studies on all of the above topics.
Georg JaegerGero LichtNorman SeyfferStefan Reitmann...
25-29页
查看更多>>摘要：While research will enable the deployment of autonomous systems in harsh and inaccessible environments, their operation may be interrupted due to unforeseen situations. A possibility to recover operation nonetheless is to employ teleoperation. However, what requirements and criteria need to be fulfilled by such a system when deployed in safety-critical operation scenarios? How can a timely and safe operation recovery be ensured? The present work aims to report our progress in developing a research platform for addressing these and similar questions.
查看更多>>摘要：Deep neural networks (DNNs) have demonstrated promising performances in handling complex real-world scenarios, surpassing human intelligence. Despite their exciting performances, DNNs are not robust against adversarial attacks. They are specifically vulnerable to data poisoning attacks where attackers meddle with the initial training data, despite the multiple defensive methods available, such as defensive distillation. However, defensive distillation has shown promising results in robustifying image classification deep learning (DL) models against adversarial attacks at the inference level, but they remain vulnerable to data poisoning attacks. This work incorporates a data denoising and reconstruction framework with a defensive distillation methodology to defend against such attacks. We leverage a denoising autoencoder (DAE) to develop a data reconstruction and filtering pipeline with a well-designed reconstruction threshold. We added carefully created adversarial examples to the initial training data to assess the proposed method's performance. Our experimental findings demonstrate that the proposed methodology significantly reduced the vulnerability of the defensive distillation framework to a data poison attack.
查看更多>>摘要：Machine Learning (ML) models are increasingly used in systems that involve physical human interaction or decision-making systems that impact human health and safety. Ensuring that these systems are safe and reliable is an important topic of current AI research. For many ML models it is unclear how a prediction (output) is arrived at from the provided features (input). Critical systems cannot blindly trust the predictions of such "black box" models, but instead need additional reassurance via insight into the model's reasoning. A range of methods exist within the field of Explainable AI (XAI) to make the reasoning of black box ML models more understandable and transparent.The explanations provided by XAI methods may be evaluated in a number of (competing) ways. In this paper, we investigate the trade-off between selected metrics for an XAI method called UnRAvEL, which is similar to the popular LIME approach. Our results show that by weighting the terms within the acquisition function used in UnRAvEL, different trade-offs can be achieved.
Douglas C. SchmidtJesse Spencer-SmithQuchen FuJules White...
43-51页
查看更多>>摘要：The rapid advent of Large Language Models (LLMs), such as ChatGPT and Claude, is revolutionizing various fields, from education and healthcare to the engineering of reliable software systems. These LLMs operate through "prompts," which are natural language inputs that users employ to query and leverage the models' capabilities. Given the novelty of LLMs, the understanding of how to effectively use prompts remains largely anecdotal, based on isolated use cases. This fragmented approach limits the reliability and utility of LLMs, especially when they are applied in mission-critical software environments. To harness the full potential of LLMs in such crucial contexts, therefore, we need a systematic, disciplined approach to "prompt engineering" that guides interactions with and evaluations of these LLMs. This paper provides several contributions to research on LLMs for reliable software systems. First, it provides a holistic perspective on the emerging discipline of prompt engineering. Second, it discusses the importance of codifying "prompt patterns" to provide a sound foundation for prompt engineering. Third, it provides examples of prompt patterns that improve human interaction with LLMs in the context of software engineering, as well as other domains. We conclude by summarizing ways in which prompt patterns play an essential role in providing the foundation for prompt engineering.
查看更多>>摘要：Smart mobility is emerging, addressing heterogeneous scenarios with high impact on technology infrastructures, solutions, and people. Safety and availability are mandatory, forcing the design of new reliable services for localization, health monitoring of the user, maintenance of vehicle, and protection of the environment. This paper proposes a container-based microservice approach to the edge computing in IoT smart mobility scenarios. Since smart mobility backends must manage a large heterogeneity of applications, the proposed approach is promising with respect to the classical solutions (based on "monolithic hardwares+software " devices), from the point of view of flexibility, upgradability, security, scalability, and reliability. A demo use case, based on industry-grade hardware and Docker, has been realized and multiple implementations of the same services have been executed in parallel, showing strong independence between them. Moreover, average delays of less than 10 ms are obtained, confirming the usability in several smart mobility (and smart city) applications.
查看更多>>摘要：The development of real-time systems is one of the areas with the highest relevance in computer science, and the number of critical systems has increased significantly. These systems considers several applications running concurrently, and inside each of those applications code might be parallelized to improve their performance and control the priority of each parallelizable task. Several efforts have been done in different programming languages to provide real-time systems with parallel programming models, whether by code extensions or annotations, or with specific features in the actual language core. Rust is a recent programming language that have quickly grown in potential and already with a large community, being continuously formed. The language is a good candidate in terms of both real-time systems and parallel programming. However, there is a lack of work that joins these two important concepts in an efficient and reliable way. In this work we aim to design and provide a framework for real-time parallel systems. We conduct a study over the existing work in other programming languages and aim to bring their advantages and useful programming models into the Rust programming language, in the format of a real-time parallel programming library.
查看更多>>摘要：This article deals with scheduling anomalies in real-time systems. We present MONANO, a POSIX user-level library allowing applications to dynamically detect a pre-identified set of real-time scheduling anomalies.The MONANO library is based on the modelling of architecture and runtime constraints. MONANO monitors during the runtime the timing behavior of the application and deduces properties needed to identify scheduling anomalies.We present also a benchmark to evaluate our approach. The benchmark is composed of several programs implementing the most frequent real-time scheduling anomalies.
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