Traffic forecasting has become a core component of Intelligent Transportation Systems. However, accurate traffic forecasting is very challenging, caused by the complex traffic road networks. Most existing forecasting methods do not fully consider the topological structure information of road networks, making it difficult to extract accurate spatial features. In addition, spatial and temporal features have different impacts on traffic conditions, but the existing studies ignore the distribution of spatial-temporal features in traffic regions. To address these limitations, we propose a novel graph neural network architecture named Attention-based Spatial-Temporal Adaptive Integration Gated Network (AST-AIGN). The originality of AST-AIGN is to obtain a spatial feature that more accurately reflects the topological structure of the road networks by embedding Graph Attention Network (GAT) into Jumping Knowledge Net (JK-Net). We propose a data-dependent function called spatial-temporal adaptive integration gate to process the diversity of feature distribution and highlight features in road networks that significantly affects traffic conditions. We evaluate our model on two real-world traffic datasets from the Caltrans Performance Measurement System (PEMS04 and PEMS08), and the extensive experimental results demonstrate the proposed AST-AIGN architecture outperforms other baselines.

Petroleum industry has started to embrace the advanced Petroleum Cyber-Physical System (CPS) technologies. Offshore petroleum CPS is particularly difficult to build, mainly due to the challenge in detecting and preventing offshore oil leaking. During the oil exploration and transportation process, the remote multi-sensing technology is typically used for leak detection, enabling the underwater modeling of an offshore petroleum CPS. However, such a technology suffers from insufficient remote sensing resources and large computational overhead. In this work, a cross entropy optimization based leak detection technique is proposed to detect the oil leak, which also facilitates the understanding of the oil leak induced marine pollution. Experimental results on a real Penglai oil spill event demonstrate that the proposed technique can effectively identify the sources of oil spills with accuracy of up to 90.78%.

In a petroleum cyber-physical system (CPS), interwell connectivity estimation is critical for improving petroleum production. An accurately estimated connectivity topology facilitates reduction in the production cost and improvement in the waterflood management. This paper presents the first study focused on computer-aided design for a petroleum CPS. A new CPS framework is developed to estimate the petroleum well connectivities. Such a framework explores an innovative water/oil index integrated with the advanced cross-entropy optimization. It is applied to a real industrial petroleum field with massive petroleum CPS data. The experimental results demonstrate that our automated estimations well match the expensive tracer-based true observations. This demonstrates that our framework is highly promising.

Metamorphic testing is an effective technique for testing "non-testable" programs. But the quality of metamorphic testing is highly depended on the selection of metamorphic relations and the test generation. This paper introduces an approach for iteratively developing metamorphic relations and producing adequate tests guided by testing and test evaluation results. The approach includes a framework for the development of metamorphic relations and tests, and a strategy for iteratively refining the relations and tests for generating adequate tests. The test adequacy evaluation is built on the evaluation of test coverage criteria, mutation testing, and testing of mutated metamorphic relations. The approach and its effectiveness are discussed through testing a Monte Carlo modeling program.

Unsupervised learning aims to discovery latent representation embedded in the observation, which is useful for data visualization, dimensionality reduction, and density modeling. Autoencoders have been successfully used to learn the latent variations in data, especially with the recent reintroduction by deep learning. For some specific tasks, there are supervised information or labels that can be used to further guide the unsupervised autoencoder model for finding latent representation. The Non-Parametrically Guided Autoencoder (NPGA) has been proved to be an effective model. It tries to utilize Gaussian Process Regression (GPR) to model the unknown mapping from unknown latent representation to extra supervised information. However for the discrete label information in classification tasks, using GPR could be unwise and inefficient. In this paper, we propose the Non-Parametrically Guided Autoencoder with Laplace Approximation (NPGA-LA) to effectively handle discrete labels. The idea of NPGA-LA is to make use of Gaussian Process Classification (GPC) rather than GPR to model the transformation between the latent space and the discrete label space. The experimental results verify the excellent performance of the newly developed method.

Absence of test oracles is the grand challenge for testing complex scientific software.Metamorphic testing is the novel technique for developing test oracles on metamorphic relations.Although it is easy to find metamorphic relations based on general guidelines and domain knowledge, the ones that can adequately test the software are difficult to be developed.This paper introduces a machine learning approach for iteratively developing metamorphic relations.The approach develops initial metamorphic relations and tests first, and then the relations and tests are refined through mining the initial test execution and evaluation results with machine learning algorithms.The approach and its effectiveness are illustrated through testing an open source discrete dipole approximation program.

Software architecture is the foundation for the development of software systems.Its correctness is important to the quality of the software systems that have been developed based on it.Formally modeling and analyzing software architecture is an effective way to ensure the correctness of software architecture.However, how to effectively verify software architecture and use the results from formal modeling and analysis is important to the application of the approach.In this paper, software architecture is modelled using high level Petri nets, and the model is then checked with a model based testing tool called MISTA, and bounded model checking tool Alloy to ensure the correctness of the model.The approach is designed as a two-phase process consisting of model-based testing and bounded model checking to ensure it is both practical and rigorous for analyzing software architecture.We illustrated the idea and procedure via modeling and analyzing the Publish-Subscribe architecture.The result has shown that combining bounded model checking with model based testing is an effective extension to ensure the development quality.