Recent advancements in Intelligent Connected Vehicle (ICV) systems highlight the critical importance of cybersecurity within these complex networks, yet they still face challenges such as difficulties in precise modeling and poor adaptability to dynamic environments. This paper introduces an innovative control approach by integrating an event-triggered Intermittent Control (IC) strategy within a Zero-Trust Framework (ZTF). This methodology selectively triggers events for vehicle identities and data that meet a predefined trust threshold during each control interval, significantly enhancing the dynamic response capability of the platoon control system. By optimizing resource allocation, this strategy ensures secure and reliable signal transmission and effectively safeguards the platoon against potential malicious node attacks. Consequently, this research offers a novel solution for achieving both security and efficiency in ICV platoon control.

Vehicular Crowdsensing (VCS) is a promising paradigm for supporting urban sensing services, where Service Providers (SPs) engage Mobile Vehicles (MVs) to perform data sensing tasks with specific objectives. However, existing studies have predominantly focused on data sensing quality in terms of data collection completeness and geographic fairness, while largely neglecting the important aspect of data freshness. Moreover, effective mechanisms for optimizing data freshness through coordination of the behaviors of both SPs and MVs are still lacking. Accordingly, this paper proposes a Freshness-Aware Data Sensing (FDS) model by considering heterogeneous data freshness, varying sensing capabilities of MVs, and limited budgets of SPs. The FDS is formulated as a two-stage game model, where SPs and MVs iteratively determine their pricing and sensing strategies in a self-interested manner to maximize their individual gains. Further, we develop a multi-agent reinforcement learning-based approach to learn the pricing strategies based on historical observations, which allows SPs to make pricing decisions without global knowledge. Additionally, given the pricing strategies of SPs, the optimal solution for each MV is derived. Finally, we build the simulation model based on realistic vehicular traces, where the simulation results demonstrate the superiority of the proposed algorithm in various scenarios.

Existing decoupled energy and carbon trading market leads to an inefficient and suboptimal operation of the distribution networks regarding economic interests and emission reduction. Corresponding to these issues, this paper designs a novel peer-to-peer (P2P) trading market of both energy and carbon emission allowance (CEA). It factors the value of transactive CEA into prosumers’ energy trading and leads to a cost-efficient decarbonization. The P2P coupled trading market is modelled as a risk-averse stochastic Stackelberg game to account for the competitive relationships between prosumers. Moreover, the approach enables prosumers to deal with risks in profits due to uncertainties from solar, load, and upstream price according to their different subjective perception of risks. Rather than directly enforcing prosumers to behave carbon-efficiently and grid-friendly, we impose a carbon-aware network charge to incentivize prosumer to adopt trading strategies that are optimal for both prosumers and the network. We illustrate that the proposed decentralized market-clearing algorithm yields a unique Stackelberg equilibrium without disclosing sensitive information of prosumers concerning operation costs and emission pattern. Results demonstrate that the proposed coupled market outperforms the traditional decoupled market in self-interest, social welfare, and emission reduction.

Real-world graphs like social networks are often evolutionary over time, whose observations at different timestamps lead to graph sequences. Modeling such evolutionary graphs is important for many applications, but solving this problem often requires the correspondence between the graphs at different timestamps, which may leak private node information, e.g., the temporal behavior patterns of the nodes. We proposed a Gromov-Wasserstein Autoregressive (GWAR) model to capture the generative mechanisms of evolutionary graphs, which does not require the correspondence information and thus preserves the privacy of the graphs' nodes. This model consists of two autoregressions, predicting the number of nodes and the probabilities of nodes and edges, respectively. The model takes observed graphs as its input and predicts future graphs via solving a joint graph alignment and merging task. This task leads to a fused Gromov-Wasserstein (FGW) barycenter problem, in which we approximate the alignment of the graphs based on a novel inductive fused Gromov-Wasserstein (IFGW) distance. The IFGW distance is parameterized by neural networks and can be learned under mild assumptions, thus, we can infer the FGW barycenters without iterative optimization and predict future graphs efficiently. Experiments show that our GWAR achieves encouraging performance in modeling evolutionary graphs in privacy-preserving scenarios.

Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions. However, these methods require a natural differentiable extension of the target discrete distribution, which often does not exist or does not provide effective guidance. In this paper, we develop a gradient-like proposal for any discrete distribution without this strong requirement. Built upon a locally-balanced proposal, our method efficiently approximates the discrete likelihood ratio via Newton’s series expansion to enable a large and efficient exploration in discrete spaces. We show that our method can also be viewed as a multilinear extension, thus inheriting the desired properties. We prove that our method has a guaranteed convergence rate with or without the Metropolis-Hastings step. Furthermore, our method outperforms a number of popular alternatives in several different experiments, including the facility location problem, extractive text summarization, and image retrieval.

Developing ontologies for the Semantic Web is a time-consuming and error-prone task that typically requires the investment of considerable manpower and resources, as well as collaborative efforts. A potentially better idea is to reuse the “off-the-shelf” ontologies, whenever possible, somehow as per certain demands and requirements. A promising way to achieve ontology reuse is through creating views of ontologies, analogous to creating views of databases, with the resulting views focusing on specific topics and content of the original ontologies. This paper explores the problem of creating views of ontologies using a uniform interpolation approach. In particular, we develop a novel and practical uniform interpolation method for creating signature-based views for ontologies specified in the description logic , a very expressive description logic for which uniform interpolation has not been fully addressed. The method is terminating and sound, and computes uniform interpolants of -ontologies by eliminating from the input ontologies the names not used in the view using a forgetting procedure. This makes it the first and so far the only approach to eliminate both concept and (non-transitive) role names from -ontologies. Despite the inherent difficulty of uniform interpolation for this level of expressivity, an empirical evaluation with a prototypical implementation show very good success rates on a corpus of real-world ontologies, and demonstrates clear algorithmic advantage over the state-of-the-art system LETHE. This is extremely useful from the semantic web perspective, as it provides knowledge engineers with a powerful tool to create views of ontologies for ontology reuse.

With the increasing market penetration of electric vehicles (EVs), the charging behavior and driving characteristics of EVs have an increasing impact on the operation of power grids and traffic networks. Existing research on EV routing planning and charging navigation strategies mainly focuses on vehicle-road-network interactions, but the vehicle-to-vehicle interaction has rarely been considered, particularly in studying simultaneous charging requests. To investigate the interaction of multiple vehicles in routing planning and charging, a routing optimization of EVs for charging with an event-driven pricing strategy is proposed. The urban area of a city is taken as a case for numerical simulation, which demonstrates that the proposed strategy can not only alleviate the long-time queuing for EV fast charging but also improve the utilization rate of charging infrastructures.Note to Practitioners—This article was inspired by the concerns of difficulties for electric vehicle (EV)’s fast charging and the imbalance of the utilization rate of charging facilities. Existing route optimization and charging navigation research are mainly applicable to static traffic networks, which cannot dynamically adjust driving routes and charging strategies with real-time traffic information. Besides, the mutual impact between vehicles is rarely considered in these works in routing planning. To resolve the shortcomings of existing models, a receding-horizon-based strategy that can be applied to dynamic traffic networks is proposed. In this article, various factors that the user is concerned about within the course of driving are converted into driving costs, through which each road section of traffic networks is assigned the corresponding values. Combined with the graph theory analysis method, the mathematical form of the dynamic traffic network is presented. Then, the article carefully plans and adjusts EV driving routes and charging strategies. Numerical results demonstrate that the proposed method can significantly increase the adoption of EV fast charging while alleviating unreasonable distributions of regional charging demand.

Resilience problems from cyber-attacks on information communication technologies exist under their wide usage. False data injection (FDI) judiciously designed by attackers may cause severe consequences such as uneconomic operation and blackouts, particularly multivector energy distribution systems (MEDS), which are closely linked and interdependent. This article addresses the cyber resilient issues of an MEDS caused by FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein metric-based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, illustrating the effectiveness of the proposed cybersecured model. The produced adaptive robust economic operation for MEDS can reduce load shedding and enhance system resilience against severe cyberattacks.

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones.

The cultural algorithm, as a dual-inheritance framework designed for optimization problems, can incorporate any population-adopted evolutionary computation technique in its population space. On the other hand, based on the Five-Elements Cycle Model derived from the ancient Chinese Five Elements (metal, wood, water, fire, earth) theory, the five-elements cycle optimization algorithm was proved to be effective in solving continuous function optimization problems. In this work, we propose a multi-objective cultural algorithm with a five-elements-cycle-optimization-based population space, where the five-element cycle model is adopted as the evolution scheme in the population space of the cultural algorithm framework. Simulation results on 12 classic benchmark problems show that the proposed algorithm can effectively solve continuous optimization functions and obtains satisfactory non-dominated solutions compared with 8 representative multi-objective algorithms.

Abstract Recent methods for 2D facial landmark localization perform well on close‐to‐frontal faces, but 2D landmarks are insufficient to represent 3D structure of a facial shape. For applications that require better accuracy, such as facial motion capture and 3D shape recovery, 3DA‐2D (2D Projections of 3D Facial Annotations) is preferred. Inferring the 3D structure from a single image is an ill‐posed problem whose accuracy and robustness are not always guaranteed. This paper aims to solve accurate 2D facial landmark localization and the transformation between 2D and 3DA‐2D landmarks. One way to increase the accuracy is to input more precisely annotated facial images. The traditional cascaded regressions cannot effectively handle large or noisy training data sets. In this paper, we propose a Mini‐Batch Cascaded Regressions (MBCR) method that can iteratively train a robust model from a large data set. Benefiting from the incremental learning strategy and a small learning rate, MBCR is robust to noise in training data. We also propose a new Cross‐Dimension Annotations Conversion (CDAC) method to map facial landmarks from 2D to 3DA‐2D coordinates and vice versa. The experimental results showed that CDAC combined with MBCR outperforms the‐state‐of‐the‐art methods in 3DA‐2D facial landmark localization. Moreover, CDAC can run efficiently at up to 110 fps on a 3.4 GHz‐CPU workstation. Thus, CDAC provides a solution to transform existing 2D alignment methods into 3DA‐2D ones without slowing down the speed. Training and testing code as well as the data set can be downloaded from https://github.com/SWJTU‐3DVision/CDAC.