Search

With the popularity of weak/relaxed memory models widely used in modern hardware architectures, the C11 standard introduced a language level weak memory model, A.K.A the C11 memory model, that allows C/C++ programs to exploit the optimisation provided by the hardware platform in memory ordering and gain benefits in efficiency. On the other hand, with the weakened memory ordering allowed, more program behaviours are introduced, among which some are counterintuitive and make it even more challenging for programmers to understand or to formally reason about C11 multithread programs. To support the formal verification of the C11 weak memory programs, several program logics, e.g. RSL, GPS, FSL, and GPS+, have been developed during the last few years. However, due to the complexity of the weakened memory model, some intricate C11 features still cannot be handled in these logics. A notable example is the lack of supporting to the reasoning about a highly flexible C11 synchronisation mechanism, the release-sequence. Recently, the FSL++ logic proposed by Doko and Vafeiadis moves one step forward to address this problem, but FSL++ only considers the scenarios with atomic update operations in a release-sequence. In this article, we propose a new program logic, GPS++, that supports the reasoning about C11 programs with fully featured release-sequences. We also introduce fractional read permissions to GPS++, which are essential to the reasoning about a large number of real-world concurrent programs. GPS++ is a successor of our previous program logic GPS+, but it comes with much finer control over the resource transmission with the newly introduced restricted-shareable assertions and an enhanced protocol system. A more sophisticated resource model is devised to support the soundness proof of our new program logic. We also demonstrate GPS++ in action by verifying C11 programs with release-sequences that could not be handled by existing program logics.

Deep learning is one of the most advanced forms of machine learning. Most modern deep learning models are based on an artificial neural network, and benchmarking studies reveal that neural networks have produced results comparable to and in some cases superior to human experts. However, the generated neural networks are typically regarded as incomprehensible black-box models, which not only limits their applications, but also hinders testing and verifying. In this paper, we present an active learning framework to extract automata from neural network classifiers, which can help users to understand the classifiers. In more detail, we use Angluin’s L* algorithm as a learner and the neural network under learning as an oracle, employing abstraction interpretation of the neural network for answering membership and equivalence queries. Our abstraction consists of value, symbol and word abstractions. The factors that may affect the abstraction are also discussed in the paper. We have implemented our approach in a prototype. To evaluate it, we have performed the prototype on a MNIST classifier and have identified that the abstraction with interval number 2 and block size 1 × 28 offers the best performance in terms of F1 score. We also have compared our extracted DFA against the DFAs learned via the passive learning algorithms provided in LearnLib and the experimental results show that our DFA gives a better performance on the MNIST dataset.

This paper proposes a multimodal deep learning method for forecasting the daily power generation of small hydropower stations that considers the temporal and spatial distribution of precipitation, which compensates for the shortcomings of traditional forecasting methods that do not consider differences in the spatial distribution of precipitation. First, the actual precipitation values measured by ground weather stations and the spatial distribution of precipitation observed by meteorological satellite remote sensing are used to complete the missing precipitation data through linear interpolation, and the gridded precipitation data covering a group of small hydropower stations are constructed. Then, considering the time lag between changes in the daily power generation of the group of small hydropower stations and precipitation, the partial mutual information method is used to estimate the “time difference” between the two, and combined with the precipitation grid data, a data set of the temporal and spatial distribution of precipitation is generated. Finally, using only the temporal and spatial distribution of precipitation and historical power generation data, a multimodal deep learning network based on a convolutional neural network (CNN) and multilayer perceptron (MLP) is constructed, and a highly accurate prediction model for the daily power generation of small hydropower stations is obtained. Taking the real power generation data of a group of small hydropower stations in southern China as an example, after considering the temporal and spatial distribution of precipitation, the prediction accuracy of the proposed method is as high as 93%, which is approximately 5.8% higher than before considering the temporal and spatial distribution of precipitation. In addition, compared with mainstream methods such as support vector regression (SVR) and the long–short-term memory network (LSTM) (the average accuracy is about 87%), and the average accuracy improvement of the proposed method is approximately 6%.

Periodic control systems used in spacecrafts and automotives are usually period-driven and can be decomposed into different modes with each mode representing a system state observed from outside. Such systems may also involve intensive computing in their modes. Despite the fact that such control systems are widely used in the above-mentioned safety-critical embedded domains, there is lack of domain-specific formal modelling languages for such systems in the relevant industry. To address this problem, we propose a formal visual modeling framework called mode diagram as a concise and precise way to specify and analyze such systems. To capture the temporal properties of periodic control systems, we provide, along with mode diagram, a property specification language based on interval logic for the description of concrete temporal requirements the engineers are concerned with. The statistical model checking technique can then be used to verify the mode diagram models against desired properties. To demonstrate the viability of our approach, we have applied our modelling framework to some real life case studies from industry and helped detect two design defects for some spacecraft control systems.