Restoring high-quality images from blurred videos is a highly challenging task, especially in severely blurred scenes. In recent years, event-based methods have achieved significant progress in video deblurring. However, the modal differences between the event and image increase the difficulty of feature fusion. Additionally, the sparsity of event makes it difficult to restore some local details. To address these issues, we propose a new video deblurring method. Firstly, we design a cross-modal collaborative attention mechanism to effectively fuse features from blurred frames and event frames, thereby deeply extracting motion information from event frames. Secondly, we utilize a diffusion model to generate spatial guiding prior feature, enhancing local details and textures. Furthermore, we propose an event-guided dynamic feature fusion module that adaptively integrates spatio-temporal information from neighboring frames. Experimental results on both synthetic and real datasets demonstrate that our method outperforms the current state-of-the-art approaches. The code is available at: https://github.com/Frank-Zhou-01/EDVD-main.

With the widespread use of distributed machine learning (DML), many IT companies have established networks dedicated to DML. Different communication architectures of DML have different traffic patterns and different requirements on network performance, which is closely related to network topology . However, traditional network topologies usually pursue general goals and are agnostic to the special communication pattern of the applications. The mismatch between network topology and the applications will directly affect the training performance. Although some studies have analyzed the effect of topology on training performance, the topologies and communication architectures involved are not comprehensive, and it is still not known which topology is appropriate for which communication architecture. This survey investigates typical topologies and analyzes whether they meet the requirements of three commonly used communication architectures (i.e., Parameter Server (PS), Tree and Ring architectures) of DML. Specifically, the topology requirements of each communication architecture and two common topology requirements (i.e., high scalability and fault tolerance) for DML are studied firstly. Next, whether these topologies meet the topology requirements is analyzed. Then, this paper discusses potential technologies and approaches to construct the appropriate scheme for each topology requirement, and then presents DMLNet , a novel network topology that suits the three communication architectures. Finally, several potential directions for future research are outlined.

LEACH-like clustering protocols focus mainly on the low-power, low-rate, and low-wakeup network applications, and work in a multiround clustering strategy that causes frequent handovers of cluster heads (CHs), thus less support for real-time services that require stable cluster topologies. Besides, these protocols are faced with respective drawbacks, such as suboptimality of selected heads, costly node-base station (BS) energy overheads, lack of runtime cluster maintenance, etc. This article proposes RANCE, a randomly centralized and on-demand clustering protocol, aiming at prolonging nodes’ clustered time to support internodes collaboration while being energy efficient in mobile ad hoc networks. First, RANCE designs a randomly centralized CH selection mechanism in which every node in the local wireless network is eligible to initiate the centralized CH selection, so that the self-organizing characteristics of mobile ad hoc nodes can be utilized for head selection optimization. Second, taking into account the wireless volatility caused by changes of topology, obstacles, signal strength, etc., the fine-grained cluster relationships maintenance is provided by means of multilevel aliveness and adaptive bidirectional heartbeat packets. Third, RANCE works in an event-driven and on-demand manner instead of a time-triggered manner in LEACH-like protocols, to reduce the impact on continuous services caused by frequent CH handovers among all nodes. Simulation results show that RANCE provides longer clustered time (over 99% of nodes’ lifetime in networks more than 100 nodes) and good clustering scalability with high consistency at minimum energy cost, and exhibits good potentials in mobile wireless environments that are infrastructureless/poor for continuous missions.

In the automatic navigation robot field, robotic autonomous positioning is one of the most difficult challenges. Simultaneous localization and mapping (SLAM) technology can incrementally construct a map of the robot’s moving path in an unknown environment while estimating the position of the robot in the map, providing an effective solution for robots to fully navigate autonomously. The camera can obtain corresponding two‐dimensional digital images from the real three‐dimensional world. These images contain very rich colour, texture information, and highly recognizable features, which provide indispensable information for robots to understand and recognize the environment based on the ability to autonomously explore the unknown environment. Therefore, more and more researchers use cameras to solve SLAM problems, also known as visual SLAM. Visual SLAM needs to process a large number of image data collected by the camera, which has high performance requirements for computing hardware, and thus, its application on embedded mobile platforms is greatly limited. This paper presents a parallelization method based on embedded hardware equipped with embedded GPU. Use CUDA, a parallel computing platform, to accelerate the visual front‐end processing of the visual SLAM algorithm. Extensive experiments are done to verify the effectiveness of the method. The results show that the presented method effectively improves the operating efficiency of the visual SLAM algorithm and ensures the original accuracy of the algorithm.

The economic, educational and vocational growth is the key factor to the unity and development of ethnic areas and population in China. Ethnic universities dedicated to minority nationalities play a key role in cultivating ethnic graduates for such a purpose. This paper proposes a Semantic Web approach for the collaborative analysis and visualization of ethnic education and vocation based on ethnic university-centric statistics. Semantic Web techniques are adopted for the modeling, storage and query of ethnic education and vocation data. A modified PageRank algorithm is derived to evaluate ethnic entrepreneurship and innovation achievements. Then, hidden relationships between educational and vocational statistics are mined by means of SPARQL query language. Finally, the collaborative analysis is visualized in a Baidu Map based Web frontend. Experiment results and demonstration of our system exhibit its applicability in analyzing and rendering ethnic education and vocation statistics.

The traditional total variation denoising method is prone to spurious edges under high noise conditions. This paper proposed a cooperative filtering method, which combines discrete wavelet transform (DWT) and total variation (TV). The total variation method is used to obtain the denoised image, and then the discrete wavelet transform is performed. The denoised image undergoes wavelet decomposition and reconstruction, could reduce the edge effect and improve the image quality. The experimental results show that the proposed method could solve the noise suppression of variational problems, at the same time, it could provide better peak signal to noise ratio and some other parameters.

Semantic Web approaches are often used for Web service description, modeling, semantics discovery, capabilities matching, etc. However, as the primary querying tool for Semantic Web, SPARQL is yet to be deeply explored to support Semantic Web service composition. Therefore the description, modeling and composition of Semantic Web services are usually two-tier. This paper extends SPARQL to support path query, so that SPARQL is used in our service composition framework which finds the top-k shortest data flows that satisfy the user constraints. Experiment results on real-world Web service datasets exhibit its applicable performance compared with service compositions using other SPARQL engines/extensions.

The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc. This paper proposes a K-means clustering algorithm based on the Binary Correlation Quantum Behaved Particle Swarm Optimization (BC-QPSO) to relieve the above shortcomings. Convergence is guaranteed in this improved K-means algorithm with probability 1 by means of the powerful global searching ability offered by BC-QPSO. The swarm fitness variance determines the transition between BC-QPSO and K-means. The experiment results on clustering analysis show that the improved K-means clustering algorithm outperforms the traditional algorithm with regard to remote sensing imaging precision.

During the execution period of composite Web services, the conventional centralized orchestration (CO) tends to incur various drawbacks such as unnecessary traffic, inappropriate dependencies, etc. Decentralized orchestration is advocated to solve these drawbacks but results in some other ones if the pure decentralized orchestration (PDO) is adopted such as high cost for component Web service proxy deployment, weak execution monitoring, etc. In this paper, by deriving the concept of affinity for component service relationship modeling and employing the novel service granule facility, we propose the decentralized orchestration with local centralized orchestration (DOLCO) to cope with the problems encountered by PDO. Experiments show that DOLCO can serve as the complement or alternative of decentralized orchestration.