Recent advances have given rise to the popularity and success of cloud computing. However, when outsourcing the data and business application to a third party causes the security and privacy issues to become a critical concern. Throughout the study at hand, the authors obtain a common goal to provide a comprehensive review of the existing security and privacy issues in cloud environments. We have identified five most representative security and privacy attributes (i.e., confidentiality, integrity, availability, accountability, and privacy-preservability). Beginning with these attributes, we present the relationships among them, the vulnerabilities that may be exploited by attackers, the threat models, as well as existing defense strategies in a cloud scenario. Future research directions are previously determined for each attribute.

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Developing full-color organic ultralong room temperature phosphorescence (OURTP) materials with continuously variable afterglow emission is of considerable practical importance in diverse optoelectronic applications but remains a formidable challenge. Here, we present an effective strategy for on-demand engineering of afterglow color in water-soluble polymeric systems via efficient phosphorescence Förster resonance energy transfer. Using a blue afterglow emitting water-soluble polymer as host and a series of fluorescent emitters with varied emissive colors as guests, afterglow emission is rationally modulated, conferring the full-color afterglow emission ranging from blue to red and even white with ultralong lifetimes up to 4.2 s and photoluminescence quantum yields of 36%.These water-soluble multicolor-emitting polymeric afterglow systems can function as OURTP security inks, and multilevel information encryption was successfully established by RGB-based multicolor security printing. These results present important guidance in developing high-performance afterglow polymers with on-demand color tuning ability for remarkable optoelectronic applications.

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High-efficiency narrowband emission is always in the central role of organic optoelectronic display applications. However, the development of organic afterglow materials with sufficient color purity and high quantum efficiency for hyperafterglow is still great challenging due to the large structural relaxation and severe non-radiative decay of triplet excitons. Here we demonstrate a simple yet efficient strategy to achieve hyperafterglow emission through sensitizing and stabilizing isolated fluorescence chromophores by integrating multi-resonance fluorescence chromophores into afterglow host in a single-component copolymer. Bright multicolor hyperafterglow with maximum photoluminescent efficiencies of 88.9%, minimum full-width at half-maximums (FWHMs) of 38 nm and ultralong lifetimes of 1.64 s under ambient conditions are achieved. With this facilely designed polymer, a large-area hyperafterglow display panel was fabricated. By virtue of narrow emission band and high luminescent efficiency, the hyperafterglow presents a significant technological advance in developing highly efficient organic afterglow materials and extends the domain to new applications.

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The performance of fruit surface defect detection is easily affected by factors such as noisy background and foliage occlusion. In this study, we choose lychee as a fruit type to investigate its surface quality. Lychees are hard to preserve and have to be stored at low temperatures to keep fresh. Additionally, the surface of lychees is subject to scratches and cracks during harvesting/processing. To explore the feasibility of the automation of defective surface detection for lychees, we build a dataset with 3743 samples divided into three categories, namely, mature, defects, and rot. The original dataset suffers an imbalanced distribution issue. To address it, we adopt a transformer-based generative adversarial network (GAN) as a means of data augmentation that can effectively enhance the original training set with more and diverse samples to rebalance the three categories. In addition, we investigate three deep convolutional neural network (DCNN) models, including SSD-MobileNet V2, Faster RCNN-ResNet50, and Faster RCNN-Inception-ResNet V2, trained under different settings for an extensive comparison study. The results show that all three models demonstrate consistent performance gains in mean average precision (mAP), with the application of GAN-based augmentation. The rebalanced dataset also reduces the inter-category discrepancy, allowing a DCNN model to be trained equally across categories. In addition, the qualitative results show that models trained under the augmented setting can better identify the critical regions and the object boundary, leading to gains in mAP. Lastly, we conclude that the most cost-effective model, SSD-MobileNet V2, presents a comparable mAP (91.81%) and a superior inference speed (102 FPS), suitable for real-time detection in industrial-level applications.

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