Event-based Compositional Reasoning of Information-Flow Security for Concurrent Systems

High assurance of information-flow security (IFS) for concurrent systems is challenging. A promising way for formal verification of concurrent systems is the rely-guarantee method. However, existing compositional reasoning approaches for IFS concentrate on language-based IFS. It is often not applicable for system-level security, such as multicore operating system kernels, in which secrecy of actions should also be considered. On the other hand, existing studies on the rely-guarantee method are basically built on concurrent programming languages, by which semantics of concurrent systems cannot be completely captured in a straightforward way. In order to formally verify state-action based IFS for concurrent systems, we propose a rely-guarantee-based compositional reasoning approach for IFS in this paper. We first design a language by incorporating ``Event'' into concurrent languages and give the IFS semantics of the language. As a primitive element, events offer an extremely neat framework for modeling system and are not necessarily atomic in our language. For compositional reasoning of IFS, we use rely-guarantee specification to define new forms of unwinding conditions (UCs) on events, i.e., event UCs. By a rely-guarantee proof system of the language and the soundness of event UCs, we have that event UCs imply IFS of concurrent systems. In such a way, we relax the atomicity constraint of actions in traditional UCs and provide a compositional reasoning way for IFS in which security proof of systems can be discharged by independent security proof on individual events. Finally, we mechanize the approach in Isabelle/HOL and develop a formal specification and its IFS proof for multicore separation kernels as a study case according to an industrial standard -- ARINC 653

Electrical Response of Mortar Saturated with NaCl Solutions under Freeze–Thaw Cycles

This paper presents the test results of electrical response of mortar saturated with sodium chloride (NaCl) solutions under freeze–thaw cycles (FTCs). To quantitatively evaluate the salt frost damage of mortar based on its electrical response, mesoscale samples are prepared to assure the uniform pore solution concentration. The reduction of electrical resistivity shows the same tendency with elastic modulus, but with less degree. The investigation shows that electrical resistivity of mortar decreases with temperature and the phase changes can be observed based on their relationship. The freezing and thawing points decreasing with increment of solution concentration can be found, but their variations with FTCs are not significant. Basically, along with frost damage development, the electrical resistivity of mortar at 23 and -28°C is decreasing with FTCs. However, for lower water-to-cement ratio and higher NaCl concentration solution exposed samples, contrary tendency are observed. In addition, with FTCs, there is no clear change for the activation energy of DI water case, whereas the decreasing tendency is observed in the cases of 5 and 15% NaCl solution. Therefore, the electrical properties are important for understanding the salt frost damage, but a comprehensive parameter to quantify the damage is still in need

Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples, without forgetting knowledge of old classes. The challenges of FSCIL lies in the limited data of new classes, which not only lead to significant overfitting issues but also exacerbates the notorious catastrophic forgetting problems. As proved in early studies, building sample relationships is beneficial for learning from few-shot samples. In this paper, we promote the idea to the incremental scenario, and propose a Sample-to-Class (S2C) graph learning method for FSCIL. Specifically, we propose a Sample-level Graph Network (SGN) that focuses on analyzing sample relationships within a single session. This network helps aggregate similar samples, ultimately leading to the extraction of more refined class-level features. Then, we present a Class-level Graph Network (CGN) that establishes connections across class-level features of both new and old classes. This network plays a crucial role in linking the knowledge between different sessions and helps improve overall learning in the FSCIL scenario. Moreover, we design a multi-stage strategy for training S2C model, which mitigates the training challenges posed by limited data in the incremental process. The multi-stage training strategy is designed to build S2C graph from base to few-shot stages, and improve the capacity via an extra pseudo-incremental stage. Experiments on three popular benchmark datasets show that our method clearly outperforms the baselines and sets new state-of-the-art results in FSCIL

Physical Model and Mesoscale Simulation of Mortar and Concrete Deformations under Freeze–Thaw Cycles

The degradation of concrete material under multiple freeze–thaw cycles is an important issue for structures in cold and wet regions. This paper proposed a physical and mechanical model to explain the deformation behavior observed in previous experiments, from internal pressure calculation to mesoscale simulation, and for both closed and open freeze–thaw tests. Three kinds of internal pressures are considered in this study: hydraulic pressure due to ice volume expansion, crystallization pressure, and cryosuction pressure due to liquid–ice interface. The hydraulic pressure model combines Power’s model with poromechanical theories, which can well explain the reverse phenomenon (from expansion to contraction) observed in the closed test. The total internal pressure will be applied in a discrete numerical method (Rigid Body Spring Model) to simulate the deformation during each cycle, as well as the unrecoverable cracking (residual strain) at the end of each cycle. The constitutive laws are also modified considering the features of those internal pressures. Finally, the deformation behaviors of mortar, mortar–aggregate interface (closed test, 30 cycles), and the concrete (open test, 300 cycles) are simulated and compared with experiment measurements, which are found in a satisfactory agreement