Incentive Decision on Safety Investment of Supply Chain of Agricultural Products in “Agricultural Super-Docking”

Since the “agriculture super-docking” mode was introduced in China in 2007, remarkable success has been made in reducing the transaction cost and improving the quality safety of agricultural products. However, the quality safety issues of agricultural products still occur frequently because both specialized farmers’ cooperatives and supermarkets have insufficient safety investment. In order to study the necessity, goal, and incentive decision schemes of safety investment in “agriculture super-docking” supply chain, three kinds of models, which include noncooperatives distributed decision-making model, centralized decision-making model, and incentive coordination models led by cooperatives and supermarkets, are, respectively, set up in this paper. Conclusions are drawn as follows: when making the uncooperative decentralized decision, both cooperatives and supermarkets have the moral risks to decrease the safety investment, but appropriate measures can achieve the coordination of the supply chain; when achieving the coordination of supply chain, the two contacts under the guidance of cooperatives and supermarkets are the same, and the schemes of distributing profits are also the same. Moreover, a practical case is given to improve the effectiveness and feasibility of the incentive decision schemes

Terminal Sliding Mode Control with Unidirectional Auxiliary Surfaces for Hypersonic Vehicles Based on Adaptive Disturbance Observer

A novel flight control scheme is proposed using the terminal sliding mode technique, unidirectional auxiliary surfaces and the disturbance observer model. These proposed dynamic attitude control systems can improve control performance of hypersonic vehicles despite uncertainties and external disturbances. The terminal attractor is employed to improve the convergence rate associated with the critical damping characteristics problem noted in short-period motions of hypersonic vehicles. The proposed robust attitude control scheme uses a dynamic terminal sliding mode with unidirectional auxiliary surfaces. The nonlinear disturbance observer is designed to estimate system uncertainties and external disturbances. The output of the disturbance observer aids the robust adaptive control scheme and improves robust attitude control performance. Finally, simulation results are presented to illustrate the effectiveness of the proposed terminal sliding mode with unidirectional auxiliary surfaces

SPA: On-Line Availability Upgrades for Parity-based RAIDs through Supplementary Parity Augmentations

In this paper, we propose a simple but powerful on-line availability upgrade mechanism, Supplementary Parity Augmentations (SPA), to address the availability issue for parity-based RAID systems. The basic idea of SPA is to store and update the supplementary parity units on one or a few newly augmented spare disks for on-line RAID systems in the operational mode, thus achieving the goals of improving the reconstruction performance while tole-rating multiple disk failures and latent sector errors simultaneously. By applying the exclusive OR operations appropriately among supplementary parity, full parity and data units, SPA can reconstruct the data on the failed disks with a fraction of the original overhead that is proportional to the supplementary parity coverage, thus significantly reducing the overhead of data regeneration and decreasing recovery time in parity-based RAID systems. In particular, SPA has two supplementary-parity coverage orientations, SPA Vertical and SPA Diagonal, which cater to user’s different availability needs. The former, which calculates the supplementary parity of a fixed subset of the disks, can tolerate more disk failures and sector errors; whereas, the latter shifts the coverage of supplementary parity by one disk for each stripe to balance the workload and thus maximize the performance of reconstruction during recovery. The SPA with a single supplementary-parity disk can be viewed as a variant of but significantly different from the RAID5+0 architecture in that the former can easily and dynamically upgrade a RAID5 system to a RAID5+0-like system without any change to the data layout of the RAID5 system. Our extensive trace-driven simulation study shows that both SPA orientations can significantly improve the reconstruction performance of the RAID5 system while SPA Diagonal significantly improves the reconstruction performance of RAID5+0, at an acceptable performance overhead imposed in the operational mode. Moreover, our reliability analytical modeling and Sequential Monte-Carlo simulation demonstrate that both SPA orientations consistently more than double the MTTDL of the RAID5 system and improve the reliability of the RAID5+0 system noticeably

Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples

There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimization framework with a surrogate model to remove (minimize) errors from the original samples, and then applied to protect the data against unknown target models. However, existing UE generation methods all rely on an ideal assumption called label-consistency, where the hackers and protectors are assumed to hold the same label for a given sample. In this work, we propose and promote a more practical label-agnostic setting, where the hackers may exploit the protected data quite differently from the protectors. E.g., a m-class unlearnable dataset held by the protector may be exploited by the hacker as a n-class dataset. Existing UE generation methods are rendered ineffective in this challenging setting. To tackle this challenge, we present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations. Furthermore, we propose to leverage VisionandLanguage Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains. We empirically verify the effectiveness of our proposed approach under a variety of settings with different datasets, target models, and even commercial platforms Microsoft Azure and Baidu PaddlePaddle. Code is available at \url{https://github.com/jiamingzhang94/Unlearnable-Clusters}.Comment: CVPR202