With the development of information technology, distributed systems have gained increased emphasis. Data consistency has become a critical concern in distributed systems because it directly affects system reliability and user experience. Data inconsistencies can arise from transaction consistency, real-time consistency, or human tampering with data. The main focus of this paper is to verify and detect the reliability of data consistency in distributed systems. To address this issue, we propose a time Petri nets model to characterize data changes within the system. To better observe system behavior, we propose a method for transforming time Petri nets into timed automata. This transformation method is used to verify and detect data consistency in distributed systems. Finally, to demonstrate the reliability of our method, we use a shopping system as an example and perform verification testing with the support of the UPPAAL tool, providing strong support for system design and implementation.

Node fusion is an important technique in neural network large model optimization. This technique can reduce the model size and accelerate the training and validation process. However, the fusion process is often accompanied by errors, resulting in a decrease in model reliability. This paper utilizes the symbol interval propagation method to derive the value intervals of nodes. And similar nodes are selected based on these intervals for fusion, with the objective of reducing errors and diminishing the size of the neural network model. Additionally, the intervals of nodes serve as a benchmark for quantifying errors in the fusion process. The experimental results show that this method can effectively reduce the fusion error and speed up the model optimization.

The neural networks are vulnerable, as they are easily affected by small perturbations on the input. So, it is crucial to ensure stability and robustness in high-security scenarios. Many approaches aim to verify the robustness of neural networks by defining robust regions. However, these approaches do not fully represent the feature regions accurately, leading to imprecise robustness boundaries. In this paper, we propose a method to capture the abstract features based on abstract interpretation theory, and then to verify the robustness of the neural networks. Firstly, samples are divided into decision-making and non-decision-making states, the decision-making states correspond to real output classifications in the abstract output domain. Then, iterating from the abstract output domain to the input domain, to obtain the abstract features associated with the decision-making states. Experimental results demonstrate the effectiveness of the obtained abstract features, and more accuracy in verifying robustness boundaries compared to the strategy of defining robust regions.

As the scale of software systems continues to increase, predicting program defects in a quick and efficient manner has become a significant research area. Recent studies have introduced deep learning models that use neural networks to extract code features and build classifiers for defect prediction. However, most existing research focuses on extracting code features at a single granularity and single level, resulting in a lack of rich code features and low prediction accuracy. To address this issue, this paper proposes a defect prediction framework based on feature fusion and alias analysis for predicting the presence of non-inferable aliases and vulnerabilities in programs. The proposed approach parses the program into two different program representations, namely Abstract Syntax Tree (AST) and Program dependency Graph (PDG), and extracts code features for feature fusion using Long Short-Term Memory (LSTM) networks and Graph Convolutional Networks (GCN), respectively. To evaluate the effectiveness of the proposed approach, the Software Assurance Reference Dataset (SARD) from the National Institute of Standards and Technology (NIST) is chosen as the experimental dataset. The classifiers constructed using the proposed approach are used to predict the presence of non-inferable aliases and vulnerabilities in programs. The experimental results demonstrate the effectiveness of the proposed approach in predicting program defects.

With the development of cloud computing and edge computing, data sharing and collaboration have become increasing between cloud edge and end. Under the assistance of edge cloud, end users can access the data stored in the cloud by data owners. However, in an unprotected cloud-edge-end network environment, data sharing is vulnerable to security threats from malicious users, and data confidentiality cannot be guaranteed. Most of the existing data sharing approaches use the identity authentication mechanism to resist unauthorized accessed by illegal end users, but the mechanism cannot guarantee the credibility of the end user’s network environment. Therefore, this article proposes an approach for trusted sharing of data under cloud-edge-end collaboration (TSDCEE), in which we verify the trustworthiness of the data requester’s network environment based on the mechanism of attribute remote attestation. Finally, this article uses model checking Spin method to formally analyze TSDCEE, and verifies the security properties of TSDCEE.

Data plays a very important role in documents. Although much effort has been made in data processing, little research has been done on data consistency. Data inconsistency may lead to data analysis errors or inaccurate analysis results. In text, mismatches of data referred to by the same label and incompatibility in the implied semantic relationship between data can lead to data errors or data relationship errors. In order to check data consistency of the goal, we rewrite the adaptive automaton algorithm and extend tree automaton to allow the construction of deterministic tree automaton to express data relationships. In the financial report of the Postal Savings Bank of China in the past three years, we use the data in the report to model adaptive automaton models and bottom-up tree automaton, which include financial data, quarterly financial data, and data from the income statement and balance sheet, and used consistency detection algorithm evaluation. Experimental results show that the automated method simplifies the verification process, provides accurate and efficient results, and ensures data consistency and reliability in the text.