Nowadays, Connected Automated Vehicles (CAVs) have emerged as powerful infrastructures for the next-generation Intelligent Transportation System (ITS) as the rapid technological advancements of communication networks and vehicular intelligence. While prospective platoon-based techniques in CAVs, the heterogeneous traffic condition poses a challenge for platoon control in the self-organized traffic bottleneck, thus making an urgent need for a practical sustainable transportation architecture. To address this problem, we propose a software defined architecture that leverages multi-agent techniques to mobile-edge computing networks for multi-vehicle adaptive platoon, which is called SD-M3ASP. The architecture supports centralized and decentralized management of vehicular edge communication resources between mobile vehicles and edge devices, and underpins sustainable vehicular platooning capabilities. Then, we propose cluster-based kinematic models by grouping vehicles into multi-vehicle clusters (MVCs) to facilitate efficient platoon control with collision avoidance. Furthermore, we propose three-stage platoon control algorithms to adaptively balance the size of MVCs and form stable platoons in heterogeneous traffic flows. The intra-platoon and inter-platoon convergence are analyzed by using the Routh stability criterion and Lyapunov technique. A CAV simulation software is developed for demonstration purposes which is available online athttps://qgailab.com/cav-sim. Extensive numerical simulation results have shown the superiority of the proposed method, which can greatly eliminate the self-organized congestion caused by heterogeneous traffic flow.

With the rapid development of wireless communication and localization technologies, the easier collection of trajectory data can bring potential data-driven value. Recently, there has been an increasing interest in how to publish trajectory dataset without revealing personal information. However, since the large-scale and real-world sequential trajectory dataset presents a heterogeneous regional distribution, the existing study ignores the relationship between privacy budget allocation and spatial characteristics, resulting in unreasonable continuity and mapping distortion, and thus lowering the utility of the synthetic dataset. To address this problem, we propose a probability distribution model named Adaptive grid-based Weighted Differential Privacy (AWDP). First, trajectories are adaptively discretized into the multi-resolution grid structures to make trajectories more uniformly distributed and less disturbed by the noise. Second, we allocate different weighted budgets for different grids according to density-based regional characteristics. Third, a spatio-temporal continuity maintenance method is designed to solve unrealistic direction- and density-based continuity deviations of synthetic trajectories. An application system is developed for demonstration purposes which is available online athttp://qgailab.com/awdp/. The extensive experiments on three datasets demonstrate that AWDP performs significantly better than the state-of-the-art model in preserving the density distribution of the original trajectories with differential privacy guarantee and high utility.

Recent developments in multiagent consensus problems have heightened the role of network topology when the agent number increases largely. The existing works assume that the convergence evolution typically proceeds over a peer-to-peer architecture where agents are treated equally and communicate directly with perceived one-hop neighbors, thus resulting in slower convergence speed. In this article, we first extract the backbone network topology to provide a hierarchical organization over the original multiagent system (MAS). Second, we introduce a geometric convergence method based on the constraint set (CS) under periodically extracted switching-backbone topologies. Finally, we derive a fully decentralized framework named hierarchical switching-backbone MAS (HSBMAS) that is designed to conduct agents converge to a common stable equilibrium. Provable connectivity and convergence guarantees of the framework are provided when the initial topology is connected. Extensive simulation results on different-type and varying-density topologies have shown the superiority of the proposed framework.

Collaborative filtering (CF) is a classical model for recommender systems. Though neural network-based CF models have been shown to perform well in some cases, they still suffer from the sparsity and cold-start issues. To address these issues, we propose a novel neural network-based CF model, termed the cross-domain explicit–implicit-mixed CF neural network (CEICFNet). The proposed model utilizes deep neural networks to learn latent factors not only from the explicit ratings and the implicit interactions but also in a cross-domain manner. In particular, domain-shared multilayer perception (MLP) networks are designed to learn the user rating latent factors and the user interaction latent factors from the explicit ratings and the implicit interactions, respectively, which act as bridges for transferring knowledge across domains. On the other hand, domain-specific MLP networks are designed to learn the item rating latent factors and the item interaction latent factors from the explicit ratings and the implicit interactions, respectively. Then, in each domain, based on the user rating latent factors and the item rating latent factors, the rating predictive representation for each user–item pair can be learned by an MLP. Similarly, the interaction predictive representation for each user–item pair can be learned. For integrating the explicit ratings and the implicit interactions, in each domain, a fully connected layer is used to automatically assign different weights to the rating predictive representations and the interaction predictive representations, based on which the final interaction probability can be generated. Extensive experiments are conducted on five datasets and the results have confirmed the effectiveness of our model.

As an important field of Distributed artificial intelligence (DAI), multi-agent systems (MASs) have attracted the attention of extensive research scholars. Consensus as the most important issue in MAS, much progress has been made in studying the consensus control of MAS, but there are some problems remained largely unaddressed which cause the MAS to lose some useful network structure information. First, multi-agent consensus protocol usually proceeds over the low-order structure by only considering the direct edges between agents, but ignores the higher-order structure of the whole topology network. Second, the existing work assumes all the edges in a topology network have the same weight without exploring the potential diversity of the connections. In this way, multi-agent systems fail to enforce consensus, resulting in fragmentation into multiple clusters. To address the above issues, this paper proposes a Motif-aware Weighted Multi-agent System (MWMS) method for consensus control. We focus more on triangle motif in the network, but it can be extended to other kinds of motifs as well. First, a novel weighted network is used which is the combination of the edge-based lower-order structure and the motif-based higher-order structure, i.e., hybrid-order structure. Subsequently, by simultaneously considering the quantity and the quality of the connections in the network, a novel consensus framework for MAS is designed to update agents. Then, two baseline consensus algorithms are used in MWMS. In our experiments, we use ten topologies of different shapes, densities and ranges to comprehensively analyze the performance of our proposed algorithms. The simulation results show that the hybrid higher-order network can effectively enhance the consensus of the multi-agent system in different network topologies.

The city's district attraction ranking plays an essential role in the city's government because it can be used to reveal the city's district attraction and, thus, help government make decisions for urban planning in terms of the smart city. The traditional methods for urban planning mainly rely on the district's GDP, employment rate, population density, information from questionnaire surveys, and so on. However, as a comparison, such information is becoming relatively less informative as the explosion of an increasing amount of urban data. What is more, there is a serious shortcoming in these methods, i.e., they are independent representations of the attraction of a district and do not take into account the interaction among districts. With the development of urban computing, it is possible to make good use of urban data for urban planning. To this end, based on taxi big data obtained from Guangzhou, China, this article proposes a district attraction ranking approach called AttractRank, which for the first time uses taxi big data for district ranking. An application system is developed for demonstration purposes. First, the entire Guangzhou city is divided into a number of districts by using constrained K-means. Second, the original PageRank algorithm is extended to integrate with the taxi's origin-destination (OD) points to establish the OD matrix, whereby the attraction ranking of each district can be calculated. Finally, by visualizing the results and case studies obtained from AttractRank, we can successfully obtain the pattern of how attractions of districts change over time and interesting discoveries on urban lives; therefore, it has wide applications in urban planning and urban data mining.

In the past few years, higher-order community detection has drawn an increasing amount of attention. Compared with the lower-order approaches that rely on the connectivity pattern of individual nodes and edges, the higher-order approaches discover communities by leveraging the higher-order connectivity pattern via constructing a motif-based hypergraph. Despite success in capturing the building blocks of complex networks, recent study has shown that the higher-order approaches unavoidably suffer from the hypergraph fragmentation issue. Although an edge enhancement strategy has been designed previously to address this issue, adding additional edges may corrupt the original lower-order connectivity pattern. To this end, this paper defines a new problem of community detection, namely hybrid-order community detection, which aims to discover communities by simultaneously leveraging the lower-order connectivity pattern and the higherorder connectivity pattern. For addressing this new problem, a new Micro-unit Modularity (MuMod) approach is designed. The basic idea lies in constructing a micro-unit connection network, where both of the lower-order connectivity pattern and the higher-order connectivity pattern are utilized. And then a new micro-unit modularity model is proposed for generating the micro-unit groups, from which the overlapping community structure of the original network can be derived. Extensive experiments are conducted on five real-world networks. Comparison results with twelve existing approaches confirm the effectiveness of the proposed method.

Recently, multi-view outlier detection has emerged as a challenging research topic in outlier detection because of complex distributions of data across different views. There are mainly three types of outliers, i.e., attribute outliers, class outliers and class-attribute outliers. Most existing multi-view outlier detection approaches only detect part of the three types of outliers in a pairwise manner across different views, which is not able to accomplish the task of multi-view outlier detection comprehensively and uniformly. Outlier detection in a pairwise manner across different views also leads to time-consuming computation. We propose a new algorithm termed Multi-view Outlier Detection in Deep Intact Space (MODDIS) to find all the three types of outliers simultaneously and avoid comparing different views in a pairwise manner. Rather than leveraging subspace clustering, the performance of which is seriously affected by the dependence of subspaces on most real datasets, neural networks are employed in MODDIS in that neural networks have a stronger representation learning ability. Meanwhile, based on the view insufficiency assumption, a multi-view intact outlierness space assumption is proposed. Based on this assumption, a multi-view latent intact space is constructed to encode outlierness information of all views, where outlierness in any view is a snapshot from some perspective. Finally, an outlier detection measurement is defined in the latent intact space. Experiments are conducted on several UCI datasets and the empirical results demonstrate the effectiveness of our proposed method.