Abstract Despite the existence of various super‐resolution (SR) methods, most of them focus on designing models for specific upscaling factors rather than fully exploiting inter‐scale correlation to improve efficiency. In contrast, multi‐scale SR methods can effectively reduce the redundancy of network parameters by aggregating the feature extraction processes corresponding to multiple scales into a unified process. The aim of this study is to enhance the compactness and efficiency of the SR model. Thus, an efficient multi‐scale SR method called the diverse branch feature refinement network (DBFRN) is proposed. By decoupling the training process and inference process based on the idea of structural re‐parameterization, multi‐branch topology is adopted to enrich multi‐scale learning and merge branches to achieve efficient inference with equivalent effects. Specifically, two re‐parameterization strategies are designed and two corresponding feature refinement blocks for different feature levels in multi‐scale SR network. Extensive experiments demonstrate that the proposed multi‐scale SR method is effective and efficient, and it can outperform advanced single‐scale methods in terms of quantity and quality.

Abstract Compared with convolutional operation, non‐local operation can directly capture long‐range dependencies and thus has a larger receptive field. However, the computation and memory consumption of non‐local operation is much higher than convolutional operation, so it cannot be used repeatedly as a general component directly. In this paper, in order to balance the accuracy and computational complexity of non‐local enhancement, the non‐local operation is simplified based on local importance‐based pooling, which can dynamically extract discriminative features during the down‐sampling process by learning adaptive weights. Such simplified non‐local enhancement is able to prevent unacceptable computational consumption caused by directly processing the entire feature maps containing a large number of features. In order to verify the effectiveness of the proposed method, 2D and 3D feature extraction blocks are constructed based on the simplified non‐local operations, and they are stacked as feature extraction networks for space‐time video super‐resolution task, which aims to increase resolution in both time and space simultaneously. Extensive experiments demonstrate that the proposed simplified non‐local networks can effectively improve the performance of space‐time video super‐resolution task both quantitatively and qualitatively.

The operating conditions of the transmission lines can be assessed through the information measured by the smart meters in the power supply bureau. Accurate classification of transmission line faults can be helpful to improve the maintenance strategy of smart grids. This paper analyzes the mechanism of the voltage loss and the phase fault of the transmission line by using the operation data collected by the smart meters from three power supply bureaus (named Bureau A, B and C), where the faults are labeled by expert systems. In this work, a novel Particle Swarm Optimization Pattern Recognition Neural Network (PSO-PRNN) classifier is built to accurately categorize the faults and its classification performance is compared with the ones of traditional K-Nearest Neighbor (KNN), Decision Tree (DT), PSO-KNN and PSO-DT classifiers. The results show that the classification accuracy of PSO-PRNN outperforms traditional classifiers when being applied to the data collected from all three bureaus. In the A power supply bureau are 83.0%, 88.7%, 82.0%, 86.9% and 96.1%, and the classification accuracy rates are 55.7%, 68.7%, 56.6%, 68.7% and 82.5%, when used to process the data of the bureau B. The classification accuracy is 57.1%, 66.4%, 57.2%, 69.0% and 82.1%, when processing the data of bureau C. The results show that the PSO-PRNN classifier is superior to the others in terms of accuracy and applicability.

In a conventional domain adaptation person Re-identification (Re-ID) task, both the training and test images in target domain are collected under the sunny weather. However, in reality, the pedestrians to be retrieved may be obtained under severe weather conditions such as hazy, dusty and snowing, etc. This paper proposes a novel Interference Suppression Model (ISM) to deal with the interference caused by the hazy weather in domain adaptation person Re-ID. A teacher-student model is used in the ISM to distill the interference information at the feature level by reducing the discrepancy between the clear and the hazy intrinsic similarity matrix. Furthermore, in the distribution level, the extra discriminator is introduced to assist the student model make the interference feature distribution more clear. The experimental results show that the proposed method achieves the superior performance on two synthetic datasets than the state-of-the-art methods. The related code will be released online https://github.com/pangjian123/ISM-ReID.

Frame interpolation is one of the most challenging tasks in the video processing field. Recent advances have demonstrated that the deep learning‐based frame interpolation methods are promising. However, the experiments show that most existing deep learning‐based methods have the same problem as traditional methods. When these algorithms handle severe occlusions, they will produce distortions, especially around the motion boundaries. To better synthesise the image of the motion areas, the authors design a mask‐guided frame synthesis model, which consists of multiple components, based on deep convolutional neural networks. The proposed model first estimates the asymmetric bi‐directional optical flows from the intermediate frame to the input frames. Then it estimates the occlusion‐aware masks, which can compensate for the optical flow inaccuracy based on optical flows and correlation information. Finally, the warped frames are adaptively fused under the guidance of the masks to generate a high‐quality intermediate frame. Furthermore, to generate more realistic video frames, they train the network model with the pixel‐based loss and the feature‐based loss in a step‐by‐step way. In the experiment, they analyse the proposed model and compare it with the high‐performance methods, both qualitative and quantitative results show that their method performs better.

Modern distributed applications are embedding an increasing degree of dynamism, from dynamic supply-chain management, enterprise federations, and virtual collaborations to dynamic resource acquisitions and service interactions across organizations. Such dynamism leads to new challenges in security and dependability. Collaborating services in a system with a Service-Oriented Architecture (SOA) may belong to different security realms but often need to be engaged dynamically at runtime. If their security realms do not have a direct cross-realm authentication relationship, it is technically difficult to enable any secure collaboration between the services. A potential solution to this would be to locate intermediate realms at runtime, which serve as an authentication path between the two separate realms. However, the process of generating an authentication path for two distributed services can be highly complicated. It could involve a large number of extra operations for credential conversion and require a long chain of invocations to intermediate services. In this paper, we address this problem by designing and implementing a new cross-realm authentication protocol for dynamic service interactions, based on the notion of service-oriented multiparty business sessions. Our protocol requires neither credential conversion nor establishment of any authentication path between the participating services in a business session. The correctness of the protocol is formally analyzed and proven, and an empirical study is performed using two production-quality Grid systems, Globus 4 and CROWN. The experimental results indicate that the proposed protocol and its implementation have a sound level of scalability and impose only a limited degree of performance overhead, which is for example comparable with those security-related overheads in Globus 4.

Service oriented architecture (SOA) allows multiple and heterogeneous data resources to be integrated within a single service while hiding the implementation details and formats of data resources from users of the service. However, data sources for a service are often distributed geographically and connected with long-latency networks; time and bandwidth consumption of data transportation may have an impact on the system performance. Dynamic data replication is a practical solution to this problem. By replicating data copies to appropriate sites, this approach aims to reduce time and bandwidth consumptions over networks. Existing strategies for dynamic replication are typically based on so-called single-location algorithms for identifying a single site for data replication. In this paper we discuss the issues with single-location strategies in large-scale data integration applications, and examine potential multiple-location schemes. Dynamic multiple-location replication is NP-complete in nature. We therefore transform the multiple-location problem into several classical mathematical problems with different parameter settings, for which efficient approximation algorithms exist. Experimental results indicate that unlike single-location strategies our multiple-location schemes are efficient with respect to access latency and bandwidth consumption, especially when the requesters of a data set are distributed over a large scale of locations.

Modern distributed applications are embedding an increasing degree of dynamism, from dynamic supply-chain management, enterprise federations, and virtual collaborations to dynamic service interactions across organisations. Such dynamism leads to new security challenges. Collaborating services may belong to different security realms but often have to be engaged dynamically at run time. If their security realms do not have in place a direct cross-realm authentication relationship, it is technically difficult to enable any secure collaboration between the services. A typical solution to this is to locate at run time intermediate realms that serve as an authentication-path between the two separate realms. However, the process of generating an authentication path for two distributed services can be very complex. It could involve a large number of extra operations for credential conversion and require a long chain of invocations to intermediate services. In this paper, we address this problem by presenting a new cross-realm authentication protocol for dynamic service interactions, based on the notion of multi-party business sessions. Our protocol requires neither credential conversion nor establishment of any authentication path between session members. The correctness of the protocol is analysed, and a comprehensive empirical study is performed using two production quality grid systems, Globus 4 and CROWN. The experimental results indicate that our protocol and its implementation have a sound level of scalability and impose only a limited degree of performance overhead, which is comparable with those security-related overheads in Globus 4.

The Web service technology enables dynamic service composition, resource utilisation and application integration in a heterogeneous computing environment. Web services can be used to compose and perform flexible and complex business flows. In practice, a Web service may create multiple service instances working for different business flows or business sessions, whilst the service instances within a business session may be created by different Web services, often designed, implemented and maintained by different organisations across different security domains. This introduces new challenges to existing security systems and solutions. For many applications ensuring security only at the level of Web services is not enough for a fine-grained level of control for multi-party collaborations because interactions amongst Web services in fact happen at the level of service instances. In this paper, we address the problem of how to secure instance-level interactions in Web services, and discuss different schemes for identifying and authenticating service instances. We present an experimental system and analyse some performance results. The experimental system implements instance-level communication control and instance authentication. The experimental results demonstrate that the overhead of execution time introduced by instance authentication is proportional to the number of the session partners within a business session.

The Web service technology allows the dynamic composition of a workflow (or a business flow) by composing a set of existing Web services scattered across the Internet. While a given Web service may have multiple service instances taking pan in several workflows simultaneously, a workflow often involves a set of service instances that belong to different Web services. In order to establish trust relationships amongst service instances, new security protocols are urgently needed. Hada and Maruyabma [2002] presented a session-oriented, multi-party authentication protocol to resolve this problem. Within a session their protocol provides a commonly shared session secret for all the service instances, thereby distinguishing the instances from those of other sessions. However, individual instances cannot be distinguished and identified using the session secret. This leads to vulnerable session management and poor threat containment. In this paper we present a new protocol design for multiparty authentication in which each service instance of a given session is provided with a unique identifier. The coordinated atomic action scheme is exploited for achieving an improved level of threat containment. We evaluate the scalability of our design by means of both experiments and an analytical model. The result shows that time consumed by the authentication process increases linearly with an increase in the number of session participants.