In recent years, the accelerated advancement of Internet of Vehicles (IoV) technology has significantly enhanced user experiences by providing intelligent services such as multimedia entertainment and autonomous driving in vehicles. However, the enforcement of regulations concerning vehicle violations in IoV environments predominantly relies on manual methods, which are both expensive and challenging. Moreover, the inherent constraints in existing surveillance systems result in regulatory blind spots. Consequently, it is imperative to develop intelligent IoV-based surveillance mechanisms to improve the efficiency of detecting and rectifying violations. In this paper, we propose a blockchain-based self-supervision model for vehicle violations that utilizes inter-vehicle reporting and voting mechanisms to enhance the detection rate of violations and reduce regulatory pressure. A forensic blockchain is introduced in the model to enable a review of the reporting results, which improves the security and reliability of the system. Additionally, more vehicles are incentivized to participate in the system through reputation-based rewards, punishments, and incentives. The system was deployed on the Hyperledger Fabric platform. Simulation experiments were conducted using Veins, SUMO, and OMNeT++. The experimental results verify the effectiveness of the model. The reporting and voting mechanism significantly inhibit violations, and the reward and reputation mechanism effectively promote the participation of vehicles.

As the application of knowledge graphs becomes increasingly widespread, the issue of knowledge graph incompleteness has garnered significant attention. As a classical type of non-euclidean spatial data, knowledge graphs possess various complex structural types. However, most current knowledge graph completion models are developed within a single space, which makes it challenging to capture the inherent knowledge information embedded in the entire knowledge graph. This limitation hinders the representation learning capability of the models. To address this issue, this paper focuses on how to better extend the representation learning from a single space to Riemannian manifolds, which are capable of representing more complex structures. We propose a new knowledge graph completion model called MRME-KGC, based on multi-view Riemannian Manifolds fusion to achieve this. Specifically, MRME-KGC simultaneously considers the fusion of four views: two hyperbolic Riemannian spaces with negative curvature, a Euclidean Riemannian space with zero curvature, and a spherical Riemannian space with positive curvature to enhance knowledge graph modeling. Additionally, this paper proposes a contrastive learning method for Riemannian spaces to mitigate the noise and representation issues arising from Multi-view Riemannian Manifolds Fusion. This paper presents extensive experiments on MRME-KGC across multiple datasets. The results consistently demonstrate that MRME-KGC significantly outperforms current state-of-the-art models, achieving highly competitive performance even with low-dimensional embeddings.

With the development of communication technology and Internet of Things, Industrial Internet of Things (IIoT) is proposed in the automation industry for complex scenarios. Blockchain is applied in IIoT to solve data security and privacy issues related to centralized data storage and processing. However, there are inevitably performance issues with throughput constraints when blockchain manages large amounts of device data. This paper proposes a blockchain-supported performance optimization framework for IIoT systems using deep reinforcement learning (DRL) methods. We model the blockchain performance optimization problem as a Markov decision process that optimizes the blockchain’s throughput by dynamically adjusting the block size and interval through DRL while satisfying security constraints. We use the double deep Q-network (DDQN) to deal with the dynamic and complexity of optimization problems due to the heterogeneity of equipment and diversified requirements. We also alleviate the overestimation problem caused by DQN. Meanwhile, we study the impact of the number of network layers and different activation units on the performance optimization method in DDQN. Finally, we prove that our work is feasible and effective through the case study based on actual IIoT scenario datasets. Experimental results demonstrate that our proposed scheme enhances blockchain performance in IIoT systems. The detailed qualitative comparison with related work demonstrates the superiority and innovation of our work and proves that it improves the shortcomings of existing work.

Recent years, the successful application of blockchain in cryptocurrency has attracted a lot of attention, but it has also led to a rapid growth of illegal and criminal activities. Phishing scams have become the most serious type of crime in Ethereum. Some existing methods for phishing scams detection have limitations, such as high complexity, poor scalability, and high latency. In this article, we propose a novel framework named phishing detection on Ethereum via augmentation ego-graph based on graph neural network (PEAE-GNN). First, we obtain account labels and transaction records from authoritative websites and extract ego-graphs centered on labeled accounts. Then we propose a feature augmentation strategy based on structure features, transaction features and interaction intensity to augment the node features, so that these features of each ego-graph can be learned. Finally, we present a new graph-level representation, sorting the updated node features in descending order and then taking the mean value of the top n to obtain the graph representation, which can retain key information and reduce the introduction of noise. Extensive experimental results show that PEAE-GNN achieves the best performance on phishing detection tasks. At the same time, our framework has the advantages of lower complexity, better scalability, and higher efficiency, which detects phishing accounts at early stage.

Blockchain is a trans-generational technology that is gradually introduced and applied in many fields because of its characteristics such as tamper-proof, traceability, and decentralization. However, the performance bottlenecks of blockchain have been one factor that hinders its practical application. This paper proposes a blockchain performance optimization framework (called LearningChain). We use a temporal convolution network to predict the transaction arrival rate of the blockchain and propose an ensemble learning-based method and a meta-learning-based method to train a blockchain performance prediction model, respectively. We design a performance scoring mechanism to dynamically tune the configuration parameters of the blockchain to optimize the blockchain performance. In addition, we collect and contribute a blockchain performance dataset (called HFBTP) for other researchers to research. The sufficient experimental results and analysis show that LearningChain can effectively optimize blockchain performance. The quantitative and qualitative comparisons with related work demonstrate the superiority and innovation of our work, LearningChain reaches state-of-the-art, is highly applicable, scalable, and can be applied to many practical blockchain-based application scenarios and different blockchain platforms. LearningChain can be complemented with other existing blockchain performance optimization tools and methods to further enhance the effectiveness of blockchain performance optimization.

Intelligent transportation system (ITS) is an important application area of software engineering. Traffic forecasting plays a pivotal role in ITS. Because of the natural graph properties of traffic road networks, graph convolutional network (GCN) is currently the mainstream method for modeling the spatial correlation of road networks. However, most GCN-based models use graph adjacency matrices that are either pre-defined based on Euclidean distances or learned through node embedding matrices. This results in the inability to model time-varying spatial relationship owing to various unexpected external factors. Moreover, traditional graph structures may not be able to fully encode complex spatial relationship. In this study, we first propose three aspects to be considered to model the spatial correlation of road networks: geospatial, functional spatial, and time-varying spatial correlations. We capture geospatial and functional spatial correlations from a global perspective and time-varying spatial correlation from a local perspective through a Time-varying graph structure generator (TVGSG). The time-varying spatial correlation of local perspectives is constructed by a hypergraph capable of capturing complex higher-order correlation. Then, we propose a framework named Adaptive Spatio-Temporal Hypergraph Convolutional Network (ASTHGCN), which combines graph diffusion convolution (GDC) and gated temporal convolutional network (Gated TCN) into a module capable of modeling spatio-temporal features simultaneously. By stacking multiple modules mentioned above, ASTHGCN is able to extract rich spatio-temporal information for advance prediction. Experiments on METR-LA and PEMS-BAY demonstrated that ASTHGCN achieved the best performance compared to the baseline methods for both short-term prediction (15 min ahead) and medium and long-term prediction (30 and 60 min ahead).

Fine-grained entity typing is crucial to improving the efficiency of research in the field of cybersecurity. However, modality limitations and type-labeling hierarchy complexity limit the construction of fine-grained entity typing datasets and the performance of related models. Therefore, in this paper, we constructed a fine-grained entity typing dataset based on multimodal information from the cybersecurity literatures and design a multimodal representation learning model based on it. Specifically, we design and introduce a new benchmark dataset called CySets to facilitate the study of new tasks and train a novel multimodal representation learning model called Cyst-MMET with multitask objectives. The model utilizes multimodal knowledge from literature and external to unify visual and textual representations by eliminating visual noise through a multi-level fusion encoder, thereby alleviating data bottlenecks and long-tail problems in the fine-grained entity typing task. Experimental results show that CySets have sharper hierarchies and more diverse labels than the existing datasets. Across all datasets, our model achieves state-of-the-art or dominant performance (3%), demonstrating that the model is effective in predicting entity types at different granularities.

The parking lot is one of the important components of the intelligent transportation system (ITS). The current parking lots mainly use instant parking, which has low parking efficiency, during peak hours, which leads to traffic congestion. To guarantee the stable operation of parking lots, we propose a blockchain-enabled parking reservation framework, called BPR. Traditional parking reservation systems may exist the condition of malicious reservations, and resulting in wasted parking spaces. Therefore, we design a reputation mechanism to manage the parking reservation behavior of vehicles and reduce the number of malicious nodes. In addition, to balance the performance of the blockchain at different times (especially during peak hours), we use deep learning (DL) to dynamically adjust the block size to make the blockchain run more efficiently and stably. We deploy the system in Hyperledger Fabric and conduct effectiveness experiments. The comprehensive evaluation results and analysis show that the proposed reputation mechanism can effectively curb malicious nodes from reserving parking spaces and reduce the waste of parking resources. And the block size will be dynamically adjusted to balance the performance of the blockchain at different periods, this method is also applicable to other blockchain performance-sensitive scenes. Finally, this paper is compared with related work to demonstrate the innovation and feasibility of this work from various aspects.

The rapid development in knowledge graph (KG) technology and its popularity in the field of artificial intelligence (AI) have significantly increased the support for similar KG-based applications. However, there is a concerning problem regarding KGs; most of them are often incomplete. This motivated us to study knowledge graph completion (KGC). Some recent studies have used graph neural networks (GNN) such as graph convolutional networks (GCN) to model graph-structured data, providing good results on KGC tasks. However, the edge weights in GCN models are controlled by degree, a measure that moderately ignores the differences among relation information. To address the above limitations and obtain better KGC, we propose a model based on graph attention networks (GATs) and contrastive learning (CL), called the CLGAT-KGC model. This model introduces the graph attention mechanism and adds different representations of entities under the same entity corresponding to different relations to enhance the entity-relation message function. Additionally, a new CL method is proposed under the CLGAT-KGC model to better learn the embedding of entities and relations in the KG domain. We have completely verified the effectiveness of this model through extensive experiments.

针海量业务过程模型管理中,往往伴随着节点过多、行为复杂易错、用户难于理解等问题,因此如何在保持原有语义的前提下对模型进行自动化的化简对业务过程模型管理具有重要作用.然而,目前的化简方法要么会损失部分行为,要么会泛化部分行为,无法在保持行为的条件下进行模型化简,这为模型质量要求较高的领域带来了一定的挑战.为解决该问题,提出一种基于完全前缀展开的业务过程模型化简算法.该算法利用完全前缀展开技术,在保证原有过程模型行为语义等价的前提下,将模型的一部分结构转化为行为等价过程树,在最理想的情况下,可以将整个模型转化为行为等价过程树.再根据过程树中的行为和语义,重构业务过程模型,从而到达化简过程模型的目的.最后通过实验表明,该算法不仅具有正确性和可行性,还可以有效降低业务过程模型的复杂度.

An electronic toll collection (ETC) system is one of the most important parts of an intelligent transportation system (ITS), but existing ETC systems are not efficient and have vehicle fee evasion complications. Owing to the traceability and tamper resistance of a blockchain, the combination of a blockchain and ETC system is a feasible way to solve the above problems. However, traditional blockchain (e.g., Bitcoin and Ethereum) has high power consumption and low efficiency. Hence, it cannot be used in ETC systems with high throughput and is not suitable for power-constrained Internet of Things (IoT) devices. In this study, therefore, we propose a blockchain architecture for the ETC system. To curb vehicle fee evasion behavior, thereby reducing the economic loss caused, we propose a vehicle behavior management mechanism based on credit value. Furthermore, to reduce the burden of storage and build a chain of evidence for auditing purposes, we propose an evidence chain framework. To protect the data security in the transaction process, we designed a data protection method to encrypt the transaction data. Additionally, this system is based on an open-source alliance blockchain framework called Hyperledger Fabric, which is more in line with the current application scenarios of ETC systems. We implemented the system on a Raspberry Pi and carried out simulations. The comprehensive evaluation results and analysis show that the system effectively reduces the number of illegal acts, completes the evidence inspection, and improves the security of the data.

Software is rapidly evolving and operates in a changing environment; therefore, in addition to software design and testing, it is essential to observe and understand the software execution behavior by modeling data recorded during the execution of the software to improve its reliability. The nested call relationship between methods during the execution of software is common, but most process-mining methods are unable to discover them, only generating flat models with low fitness. Meanwhile, it is easy to generate "spaghetti-like" models with low comprehensibility when dealing with complex software execution data. This paper proposes a component-based hierarchical software behavior model discovery method that can discover hierarchical nested call structures during software runtime, improving the fitness of the model; meanwhile, the proposed method partitions the discovery model into several parts by component information to improve the comprehensibility of the model, which can also reflect the interaction behavior within and between components. The proposed approach was implemented in a process mining toolkit. Using real-life software event logs and public datasets, we demonstrated that compared with other advanced process mining techniques, our approach can visualize actual software execution behavior in a more accurate and easy-to-understand way while balancing time performance.