In vehicular edge computing (VEC) networks, by implementing content caching and V2X connectivity, road side unit (RSU) and nearby vehicles can serve as platforms for rapid data retrieval to address mobile traffic explosion. However, due to the dynamic and multi-constrained environment consisting of heterogeneous vehicles and RSU, it is challenging to meticulously plan cooperative caching policies. Additionally, due to the diversity of contents and the mobility of vehicles, the caching policy space is massive, which can be fatal for vehicles with limited computing and energy. In this paper, we formulate cooperative content caching in VEC networks as Markov decision process (MDP), configuring caching policies for vehicles and RSU. Our aim is to minimize Lyapunov drift and long-term delay. To address the massive caching policies, we propose a two-stage deep reinforcement learning (TS-DRL) algorithm. In the first stage, an improved ant colony algorithm is used to generate unilateral suggestions and construct action space to avoid the curse of dimensionality. In the second stage, we combine the Noisy Net and Double Deep Q-Learning Network to avoid overestimating value and efficient exploration problem. Simulation results show that TS-DRL outperforms advanced algorithms in terms of delay and cache hit rate.

Vehicular edge computing (VEC) emerges as a promising paradigm for processing computing-intensive parallel vehicular tasks, where vehicular tasks can be offloaded to the edge nodes [e.g., roadside units (RSUs)] to seek less computing delay. Considering the impact of computation services on offloading efficiency, there are several works that jointly study the decision making of task offloading and service caching. However, the existing works fail to consider the time-varying service requests and ignore the time-slots correlation of the computation services. To bridge the gap, this work designs a service-aware parallel task offloading approach, which is the first work to jointly explore time-varying computation services and task offloading based on real-world vehicular trajectory data in VEC networks. Specifically, we first propose a computation service prediction algorithm using the real-world vehicular trajectory data. Guided by this, RSUs flexibly precache computation services. Then, we propose a learning-based parallel task offloading algorithm, which allows vehicles to make offloading decisions based on the history of the edge selections. Furthermore, we conduct simulations to validate the proposed algorithm. The results demonstrate that the proposed algorithm reduces task delay by 45%, 58%, and 55% compared to the algorithms without service-aware computation offloading under various CPU cycles, task numbers, and time slots.

With deepening urbanization and Internet of Vehicles (IoV) applications, the number of private cars has been increasing in recent years. However, because the surging number of private cars is not compatible with limited road resources, private car users have had unsatisfactory commute experiences during their daily travel. In this work, we focus on improving private car users’ commute experience based on an analysis of IoV trajectory data in a privacy-preserving way. Our idea is based on the following observations: 1) the commute experience of private car users is closely related to the departure time and the travel cost and 2) most travel costs are spent on urban hot zones. Motivated by these findings, we propose a novel blockchain-enabled model named Deep Improving Commute Experience (DeepICE) to improve private car users’ commute experience by predicting when to depart and when to arrive. In this model, a blockchain with a consensus mechanism is developed to address private car user privacy concerns. In addition, we propose a multitask learning-enabled graph convolution network (GCN) method to capture the highly complex features and relations between two tasks, i.e., the departure time and travel cost, and then develop the model to predict these two tasks. The experimental results demonstrate the superior performance of our proposed model compared to existing approaches. Our model can be applied to efficiently enhance private car users’ commute experience.

For tolerating Byzantine faults of both the terminal and communication components in self-stabilizing clock synchronization, the two-dimensional self-stabilizing Byzantine-fault-tolerant clock synchronization problem is investigated and solved. By utilizing the time-triggered (TT) stage provided in the underlying networks as TT communication windows, the approximate agreement, hopping procedure, and randomized grandmasters are integrated into the overall solution. It is shown that with partitioning the communication components into 3 arbitrarily connected subnetworks, efficient synchronization can be achieved with one such subnetwork and less than 1/3 terminal components being Byzantine. Meanwhile, the desired stabilization can be reached for the specific networks in one or several seconds with high probabilities. This helps in developing various distributed hard-real-time systems with stringent time, resources, and safety requirements.

The authenticated broadcast is simulated in the bounded-degree networks to provide efficient broadcast primitives for building efficient higher-layer Byzantine protocols. A general abstraction of the relay-based broadcast system is introduced, in which the properties of the relay-based broadcast primitives are generalized. With this, fault-tolerant propagation is proposed as a building block of the broadcast primitives. Meanwhile, complementary systems are proposed in complementing fault-tolerant propagation and localized communication. Analysis shows that efficient fault-tolerant propagation can be built with sufficient initiation areas. Meanwhile, by integrating fault-tolerant propagation and localized communication, efficient broadcast primitives can be built in bounded-degree networks.

To improve the overall efficiency and reliability of Byzantine protocols in large sparse networks, we propose a new system assumption for developing multi-scale fault-tolerant systems, with which several kinds of multi-scale Byzantine protocols are developed in large sparse networks with high system assumption coverage. By extending the traditional Byzantine adversary to the multi-scale adversaries, it is shown that efficient deterministic Byzantine broadcast and Byzantine agreement can be built in logarithmic-degree networks. Meanwhile, it is shown that the multi-scale adversary can make a finer trade-off between the system assumption coverage and the overall efficiency of the Byzantine protocols, especially when a small portion of the low-layer small-scale protocols are allowed to fail arbitrarily. With this, efficient Byzantine protocols can be built in large sparse networks with high system reliability.

Tail-sitter unmanned aerial vehicles (UAVs) have broad application prospects since they merge advantages of both fixed-wing UAVs and rotary-wing UAVs. However, there exist great challenges in the transition maneuvers due to model un-certainties and external disturbances. Aimed at these problems, a robust transition controller based on incremental nonlinear dynamic inversion (INDI) is developed for a tail-sitter UAV in this paper. Different from existing works, the controller mainly concerns the transition pitch angle and altitude, because the altitude is more intuitive then flight speed in indicating whether a transition is successful or not. The robustness of the developed transition controller is analyzed with consideration of error terms exist in the closed-loop system, which were generally omitted in existing INDI flight control works. With some reasonable assumptions, it is proven that the tracking errors can converge into a specified neighbourhood of the origin in a finite time by choosing appropriate controller parameters. Numerical simulations demonstrate the robustness of the controller in handling model uncertainties and external disturbances.

MOTIVATION: DNA N4-methylcytosine (4mC) modification is an important epigenetic modification in prokaryotic DNA due to its role in regulating DNA replication and protecting the host DNA against degradation. An efficient algorithm to identify 4mC sites is needed for downstream analyses. RESULTS: In this study, we propose a new prediction method named SOMM4mC based on a second-order Markov model, which makes use of the transition probability between adjacent nucleotides to identify 4mC sites. The results show that the first-order and second-order Markov model are superior to the three existing algorithms in all six species (Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterruneus and Geobacter pickeringii) where benchmark datasets are available. However, the classification performance of SOMM4mC is more outstanding than that of first-order Markov model. Especially, for E.coli and C.elegans, the overall accuracy of SOMM4mC are 91.8% and 87.6%, which are 8.5% and 6.1% higher than those of the latest method 4mcPred-SVM, respectively. This shows that more discriminant sequence information is captured by SOMM4mC through the dependency between adjacent nucleotides. AVAILABILITY AND IMPLEMENTATION: The web server of SOMM4mC is freely accessible at www.insect-genome.com/SOMM4mC. CONTACT: [email protected] or [email protected].

As social animals, attending group activities is an indispensable part in people's daily social life, and it is an important task for recommender systems to suggest satisfying activities to a group of users. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group. Conventional group recommendation methods applied a predefined strategy for preference aggregation. However, these static strategies are too simple to model the real and complex process of group decision-making, especially for occasional groups which are formed ad-hoc. Moreover, group members should have non-uniform influences or weights in a group, and the weight of a user can be varied in different groups. Therefore, an ideal group recommender system should be able to accurately learn not only users' personal preferences but also the preference aggregation strategy from data. In this paper, we propose a novel group recommender system, namely SIGR (short for "Social Influence-based Group Recommender"), which takes an attention mechanism and a bipartite graph embedding model BGEM as building blocks. Specifically, we adopt an attention mechanism to learn each user's social influence and adapt their social influences to different groups and develop a novel deep social influence learning framework to exploit and integrate users' global and local social network structure information to further improve the estimation of users' social influences. BGEM is extended to model group-item interactions. In order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose two model optimization approaches to seamlessly integrate the user-item interaction data. We create two large-scale benchmark datasets and conduct extensive experiments on them. The experimental results show the superiority of our proposed SIGR by comparing with state-of-the-art group recommender models.

In order to solve the problem of risk assessment for mountainous geological disaster in southwest of China, we selected Wenchuan county as the study area, where the geological disasters happen frequently. the digital elevation model (DEM), and other geographic data of Wenchuan county were utilized to evaluate the risk of geological disasters. Firstly, the weights of factors for geological hazard susceptibility were identified using analytic hierarchy process (AHP). Secondly, the fuzzy distinguish matrix based on the strength of membership function was established by combining AHP with fuzzy comprehensive evaluation (FCE). Thirdly, the geological hazard risk system was constructed according to the elevation, slope, distance from the river or the fault zone. Finally, we divided the study area into three risk types: high, moderate, and low. Our research results showed that the landslide numbers of high, moderate, and low risk are 11759, 16889, and 6075, respectively, and the corresponding percentages of area in Wenchuan county are 34%, 49%, 17%, respectively. Our results were in line with the historical disaster data. Therefore, the governments should pay more attentions to the geological disasters of these towns.

The prop-hanging control of the fixed-wing aircraft can enable the vehicle to improve the cruise performance and hover ability. The prop-hanging control of the thrust vector vehicle would be a challenging problem, for the reason that it is a system with properties of inherent instability, coupling, non-linearity and non-minimum phase characteristics. To deal with these complex problems, a hybrid Nonlinear Dynamic Inversion (NDI) method is proposed in this paper. Inspired by NDI, Incremental NDI and angular acceleration feedback control methods, the hybrid NDI method includes three parts: feedforward control, proportional control and logical integral control, which can enable the system to have a rapid response in the transient phase and a satisfactory performance in the steady state. A numerical simulation is conducted to test the performance and the robustness of the proposed control method, and a Hardware-in-the-loop simulation is also conducted to check the practicability and effectiveness in the actual application. Simulation results show that the proposed control method can effectively improve system robustness and is practical.