Thai Son Hoang

Alistair Sirman, Fleur Conway, Jessica Ciupa et al.

Neural network controllers for autonomous decision-making are well-established in cyber-physical systems, yet their deployment in safety-critical healthcare settings remains largely unverified. This paper presents a methodology and case study for the formal verification of a neural network controller for antibiotic dosing, motivated by the challenge of systems that must be simultaneously adaptive and provably safe across unbounded time horizons. We construct a simplified yet clinically-interpretable model that tracks drug concentration, body temperature, and white blood cell count. Vancomycin is selected as a representative antibiotic, widely prescribed for severe infections yet carrying a narrow therapeutic window, where supratherapeutic concentrations risk nephrotoxicity and subtherapeutic dosing risks treatment failure. A supervised neural network controller is trained on synthetic clinician-style dosing data. We establish formal verification of input-output safety properties, specifically verifying a property of a neural network that implies an infinite-horizon proof that automated dosing never exceeds the supratherapeutic boundary. This system property is proven in Rocq using the Vehicle interactive theorem prover back-end to integrate the different proof systems. The end result is a verification pipeline that allows for a wide variety of treatment approaches whilst maintaining safety for each specific patient.

Haider AL-Shareefy, Michael Butler, Thai Son Hoang

This research paper presents an approach to enhancing the predictive capability of architects in the design and assurance of systems, focusing on systems operating in dynamic and unpredictable environments. By adopting a systems approach, we aim to improve architects' predictive capabilities in designing dependable systems (for example, ML-based systems). An aerospace case study is used to illustrate the approach. Multiple factors (challenges) influencing aircraft detection are identified, demonstrating the effectiveness of our approach in a complex operational setting. Our approach primarily aimed to enhance the architect's predictive capability.

Long Doan, Linh The Nguyen, Nguyen Luong Tran et al.

We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. We conduct experiments comparing strong neural baselines and well-known automatic translation engines on our dataset and find that in both automatic and human evaluations: the best performance is obtained by fine-tuning the pre-trained sequence-to-sequence denoising auto-encoder mBART. To our best knowledge, this is the first large-scale Vietnamese-English machine translation study. We hope our publicly available dataset and study can serve as a starting point for future research and applications on Vietnamese-English machine translation.

Usman Sanwal, Thai Son Hoang, Luigia Petre et al.

Biology offers many examples of large-scale, complex, concurrent systems: many processes take place in parallel, compete on resources and influence each other's behavior. The scalable modeling of biological systems continues to be a very active field of research. In this paper we introduce a new approach based on Event-B, a state-based formal method with refinement as its central ingredient, allowing us to check for model consistency step-by-step in an automated way. Our approach based on functions leads to an elegant and concise modeling method. We demonstrate this approach by constructing what is, to our knowledge, the largest ever built Event-B model, describing the ErbB signaling pathway, a key evolutionary pathway with a significant role in development and in many types of cancer. The Event-B model for the ErbB pathway describes 1320 molecular reactions through 242 events.

Naoto Sato, Hironobu Kuruma, Masanori Kaneko et al.

In recent years, a number of methods for verifying DNNs have been developed. Because the approaches of the methods differ and have their own limitations, we think that a number of verification methods should be applied to a developed DNN. To apply a number of methods to the DNN, it is necessary to translate either the implementation of the DNN or the verification method so that one runs in the same environment as the other. Since those translations are time-consuming, a utility tool, named DeepSaucer, which helps to retain and reuse implementations of DNNs, verification methods, and their environments, is proposed. In DeepSaucer, code snippets of loading DNNs, running verification methods, and creating their environments are retained and reused as software assets in order to reduce cost of verifying DNNs. The feasibility of DeepSaucer is confirmed by implementing it on the basis of Anaconda, which provides virtual environment for loading a DNN and running a verification method. In addition, the effectiveness of DeepSaucer is demonstrated by usecase examples.

Simon Hudon, Thai Son Hoang, Jonathan S. Ostroff

We present Unit-B, a formal method inspired by Event-B and UNITY. Unit-B aims at the stepwise design of software systems satisfying safety and liveness properties. The method features the novel notion of coarse and fine schedules, a generalisation of weak and strong fairness for specifying events' scheduling assumptions. Based on events schedules, we propose proof rules to reason about progress properties and a refinement order preserving both liveness and safety properties. We illustrate our approach by an example to show that systems development can be driven by not only safety but also liveness requirements.

T. S. Hoang, L. Voisin, A. Salehi et al.

The Theory plug-in enables modellers to extend the mathematical modelling notation for Event-B, with accompanying support for reasoning about the extended language. Previous version of the Theory plug-in has been implemented based on Rodin 2.x. This presentation outline the main improvements to the The- ory plug-in, to be compatible with Rodin 3.x, in terms of both reliability and us- ability. We will also present the changes that were needed in the Rodin core to accommodate the Theory plug-in. Finally, we identify future enhancements and research directions for the Theory plug-in.

David Basin, Andreas Fürst, Thai Son Hoang et al.

Integrating formal methods into industrial practice is a challenging task. Often, different kinds of expertise are required within the same development. On the one hand, there are domain engineers who have specific knowledge of the system under development. On the other hand, there are formal methods experts who have experience in rigorously specifying and reasoning about formal systems. Coordination between these groups is important for taking advantage of their expertise. In this paper, we describe our approach of using generic instantiation to facilitate this coordination. In particular, generic instantiation enables a separation of concerns between the different parties involved in developing formal systems.