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The current technological development is SD-WAN (Software-Defined Wide Area Network) which provides highperformance access for users located far from the head office so as to allow faster network connections and has been facilitated automation techniques for branch offices. This research solves the problem of XYZ company because it is known that the company requires network connectivity with a high SLA (Service Level Agreement) and no downtime in the information exchange process. This research hypothesis assumes that using SD-WAN would be ideal and the problems with XYZ company were resolved. The purpose of this research is the implementation of a WAN network using SD-WAN technology against two ISPs on the FortiGate router, as well as testing QoS (Quality of Service) that has been configured using the BGP (Border Gateway Protocol) routing method. This research plan consists of ISP-A using IP-VPN (Internet Protocol-Virtual Private Network) and ISP-B using broadband Internet. The test scenario was carried out using 3 methods, namely Full Service Scenario, Fail Over Scenario-1 when the IP VPN service is down and Fail Over Scenario-2 when the broadband Internet service is down. The final results of the research have obtained \"Satisfactory\" results for both services, including the average index on ISP-A and ISP-B of 3.7.

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved. In this paper, we attempt to solve this problem from the perspective of network architecture design and training data synthesis. Specifically, for the network architecture design, we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, and then plug it as the main building block into the widely-used image-to-image translation UNet architecture. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and processed camera sensor noises) and resizing, and also involves a random shuffle strategy and a double degradation strategy. Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability. We believe our work can provide useful insights into current denoising research. The source code is available at https://github.com/cszn/SCUNet.

Although most behavioral security studies focus on organizational in-role behaviors such as information security policy (ISP) compliance, the role of organizational extra-role behaviors—security behaviors that benefit organizations but are not specified in ISPs—has long been overlooked. This study examines (1) the consequences of organizational in-role and extra-role security behaviors on the effectiveness of ISPs and (2) the role of formal and social controls in enhancing in-role and extra-role security behaviors in organizations. We propose that both in-role security behaviors and extra-role security behaviors contribute to ISP effectiveness. Furthermore, based on social control theory, we hypothesize that social control can boost both in- and extra-role security behaviors. Data collected from practitioners—including information systems (IS) managers and employees at many organizations—confirmed most of our hypotheses. Survey data from IS managers substantiated the importance of extra-role behaviors in improving ISP effectiveness. Paired data, collected from managers and employees in the same organizations, indicated that formal control and social control individually and interactively enhance both in- and extra-role security behaviors. We conclude by discussing the implications of this research for academics and practitioners, along with compelling future research possibilities.

This paper studies adaptive output-feedback tracking for a class of typical uncertain nonlinear systems. In the context of unknown control direction, a generic uncertainty encountered inevitably in practice is adequately taken into consideration, i.e., the ISpS (input-to-state practically stable) inverse dynamics acting as the dynamic differences between the real plants and the models. Besides, the systems in question also permit two critical ingredients, i.e., unmeasured-state dependent nonlinearities and arbitrary function-of-output growth on unknown system nonlinearities. The three ingredients together largely challenge the feasibility/availability of practical tracking by means of output feedback. Nevertheless, a new control strategy is proposed by flexibly integrating the dynamic compensation based on Nussbaum-type gain, backstepping design technique together with the refined pseudo-sign and pseudo-dead-zone functions that were introduced for the first time in our previous studies. The two refined functions, which are sufficiently smooth, can moderately avoid the use of smooth domination/treatment in control design and can potentially render the attained control strategy tighter and less conservative. Moreover, to keep the order of the closed-loop system at a low level, an n -dimensional filter with a dynamic high gain is delicately devised instead of a 2 n -dimensional one used in the relevant literature. It turns out that the proposed adaptive output-feedback controller is capable of guaranteeing the global boundedness of all states of the resulting closed-loop system, while steering the system tracking error to enter, in finite time, a prescribed λ-neighborhood of the origin and keeping it inside thereafter. A simulation example is provided to demonstrate the proposed approach.