To address the inefficiency of traditional methods and the high computational overhead of deep learning models in sporopollen spore recognition on the Tibetan Plateau, this paper proposes a lightweight object detection model, YOLOv11-TinyED. The model incorporates two innovative designs: 1) the SEA attention module and SEAConv convolution, which dynamically enhance critical feature channels, and 2) the Hybrid Head Self-Attention (HHSA) module, balancing computational cost and feature diversity. The effectiveness of the model is validated on the constructed QT-Sporopollen dataset (22 classes) from the Tibetan Plateau. Experimental results demonstrate that the model achieves a compact parameter size of only 1.83 MB, delivers real-time inference at 83 FPS on LubanCat, and attains an$\text{m A P} {@} \text{0. 5}$of$\text{9 9. 4 3 \%}$, significantly outperforming other lightweight models. This research provides an efficient solution for edge-device-based intelligent sporopollen spore recognition. Code: https://github.com/HeHuangAI/YOLOv11-TinyED.

Reconstruction of 3D roof from airborne LiDAR point clouds is an important task in the field of remote sensing and photogrammetry. Due to the high noise, large volume, and structural complexity inherent in LiDAR point clouds, traditional point cloud processing methods have limited performance in terms of accuracy and robustness. Most existing methods rely on corner point detection and edge prediction to extract and reconstruct roof structures. However, corner points are often sparsely distributed and difficult to locate precisely, resulting in noticeable geometric deviations and incomplete structural reconstruction. Therefore, generating high-quality 3D roof from complex LiDAR point clouds is still a challenging problem to be solved. To address these issues, we propose a novel roof reconstruction method RR-Net that combines edge segmentation and wireframe generation. Firstly, an innovative unified point cloud edge segmentation network was developed, achieving integrated edge detection, segmentation, and denoising. Secondly, based on the segmentation results from this network, an efficient and robust pipeline for wireframe generation and roof reconstruction was designed, enabling automated transformation from airborne LiDAR point clouds to roof models. Extensive experiments on the Building dataset demonstrate that RR-Net can efficiently and accurately reconstruct roofs from airborne LiDAR point clouds. RR-Net achieves 95.7% accuracy in edge detection and reduces the chamfer distance (CD) error of the wireframe to 0.021. The results of RR-Net are publicly available at: https://yecoxu.github.io/publications/RR-Net/.

High-quality image generation is an important topic in digital visualization. As a sub-topic of the research, color transfer is to produce a high-quality image with ideal color scheme learned from the reference one. In this article, we investigate the mainstream methods of color transfer to provide a survey that introduces the related theories and frameworks. Such methods can be divided into three categories: statistical color transfer, semantic-based color transfer, and color transfer for special target. For these mainstream technical routes, we discuss the related research background, technical details, and representative methods. We also exhibit some new trends of the topic according to recent progress. Based on the comparisons, we discuss the unsolved issues of color transfer and potential solutions in future work.

"Thangka", is a word in the Tibetan language that refers to a kind of scroll painting mounted on silk. Thangka art, which is a very cherished and intangible cultural heritage, has a long history and distinctive characteristics. As an important prerequisite for digital protection, the research on how to classify the Thangka images quickly and accurately has become a problem of concern to scholars from in various fields. Due to the high similarity and complex structure of the Thangka image, manual classification needs to consume a large number of human resources with sufficient knowledge reserves. To improve the efficiency and accuracy of Thangka classification, researchers began to focus on computer-based machine learning and deep learning technology to complete the Thangka image classification task. Due to the complexity of the Thangka image and the lack of training samples, the existing classification methods cannot well complete the Thangka classification task. To solve the above problems, this paper proposes a fine-grained classification method Tk-SENet for Thangka images based on SENet. This method introduces the dual mechanism of spatial attention and channel attention and uses different sizes of convolution kernels to perform convolution operations in images and gives different weights according to the importance of channels and regions, which improves the classification efficiency. The pooling method in the squeeze operation and the activation function in the excitation operation are optimized to improve the classification accuracy. In the training process, the unique training method of the Thangka image is used to prepare for the excellent completion of the fine-grained classification task of the Thangka image. The experimental results show that the improved network model improves the classification accuracy by 3.6658% compared with SENet. Compared with AlexNet, ZFNet, VGG16, VGG19, ResNet-152 , DenseNet and SKNet, the accuracy increases by 6.7747%, 6.2804%, 6.6528%, 5.2586%, 6.0404% , 5.6962% and 2.9241% respectively. At the same time, compared with the existing classification methods of Thangka images, the proposed method can not only accurately identify the categories of Thangka statues, but also identify the production process types of Thangka images. Therefore, this method is more suitable for the fine-grained classification of Thangka images and provides strong technical support for the digital protection of intangible cultural heritage.

The emergence of the cloud storage has brought great convenience to people’s life. Many individuals and enterprises have delivered a large amount of data to the third-party server for storage. Thus, the privacy protection of data retrieved by the user needs to be guaranteed. Searchable encryption technology for the cloud environment is adopted to ensure that the user information is secure with retrieving data. However, most schemes only support single-keyword search and do not support file updates, which limit the flexibility of the scheme. To eliminate these problems, we propose a blockchain-enabled public key encryption scheme with multi-keyword search (BPKEMS), and our scheme supports file updates. In addition, smart contract is used to ensure the fairness of transactions between data owner and user without introducing a third party. At the data storage stage, our scheme realizes the verifiability by numbering the files, which ensures that the ciphertext received by the user is complete. In terms of security and performance, our scheme is secure against inside keyword guessing attacks (KGAs) and has better computation overhead than other related schemes.

Transferring a photo to a stylised image with beautiful texture has become one of the most popular topics in computer vision and the application of image processing. Controlling the stroke size of the texture is one of the challenging problems in this task. Recent representative methods for such problem introduce a pyramid model to regulate receptive fields in the network. Meanwhile, dilated convolutions are proved to be a very efficient way to adjust receptive fields without losing resolution. By combining the advantages of both approaches and making special optimisation for VGG19 model for style transfer tasks, the authors propose to exploit dilated convolutions to extract texture information endowing the network with stroke controllable. Several sets of contrast experiments were conducted and results show that their algorithm can generate more attractive stylisation images and control stroke size flexibly. It demonstrates the superiority of applying dilated convolutions as a texture extraction method for maintaining more texture information and controlling stroke size.

RAID codes are extensive developed to supply high reliability and high available for storage systems in modern data center. For example, RAID-0, RAID-4, RAID-5 and RAID-6 are wide deployed in in-production storage systems, of which RAID-6 codes are popular with the ability for tolerate two-disk-failure. The paper focus on performance evaluation of storage systems powered by RAID-6, firstly propose an analytical model to measure encoding, decoding and updating complexity, which is translated into XOR operation numbers for storage systems, and quantify the complexity of RDP and RP-RDP (i.e., two RAID-6 data layout schemes) using the formulated analytical model. Computational complexity of RAID code can significantly influence the write performance of storage systems, i.e., optimal computational complexity achieve better storage performance. The numerical results show RP-RDP, with optimal updating complexity, achieve better I/O balancing than RDP. For example, RP-RDP speeds up the load balancing ratio of RDP by a factor of up to 2.95 in the case of Random-SW.

In this paper, the concepts of weighted transducers over strong bimonoids and their input-output-functions are introduced. Further more, the input-functions and output-functions induced by the input-output-functions of weighted transducers over strong bimonoids are given. It is the most important that the input-functions and output-functions of weighted transducers over strong bimonoids can be realized by weighted finite automata over strong bimonoids, and the realization does not depend on the distributive law, which also embodies the applications of weighted finite automata over strong bimonoids.