In end-to-end data sharing, data are directly distributed to data receivers and stored on their terminals, making it hard to ensure forward security because receivers whose permissions have been revoked may still access previously shared data. To address these challenges, we propose an attribute-key-free CP-ABE scheme, aimed at securely binding data with access policies while ensuring forward security. Specifically, the decryption process in our scheme is delegated to the attribute authorities, which adopt the user’s real-time attribute values to decrypt the ciphertext. To prevent the honest-but-curious attribute authorities from accessing the plaintext, the ciphertext is re-encrypted with a one-time key before being sent to the attribute authorities. Furthermore, to prevent sensitive information from being inferred through the policy, we design a policy-hiding mechanism to conceal attribute values. Through these mechanisms, it can be ensured that the data subject always has control over his or her personal data during the end-to-end data-sharing process. We evaluate the performance of our scheme through both theoretical analysis and comparative experiments, and the results show our scheme’s effectiveness.

The widespread use of unmanned aerial vehicles brings both convenience and potential security risks, posing significant challenges for accurate UAV identification. To address the limitations of existing deep learning-based radio frequency fingerprinting methods in terms of computational complexity and model adaptability, this paper proposes an innovative hybrid model that combines Convolutional Neural Networks and Transformers to exploit both local and global features of RF signals fully. Our model consists of a Local Block and a Global Block. The Local Block employs Partial Convolution for feature extraction, PointWise Convolution to enhance feature representation, and the Squeeze-and-Excitation module to adaptively emphasize critical features, thereby improving local feature expressiveness. The Global Block comprises Unfold, Super Token Transformer Block, and Fold, which together enable effective modeling of global dependencies through signal unfolding, spatiotemporal transformations, and reconstruction. Experimental results show that under signal-to-noise ratio (SNR) conditions ranging from –5 dB to 20 dB, our method achieves an average recognition accuracy of 96.89%, with powerful performance under low SNR conditions. Furthermore, experiments demonstrate the model’s robustness with limited data. Even with only 1,000 training samples, the model maintains an accuracy of 92.14%. When incorporating Mixup data augmentation, high classification performance is sustained with just 500 training samples. These results highlight the method’s strong adaptability and practical potential in complex environments. Our codes and models are available at https://github.com/Zhoukaijie-hy/hybrid-model.

BACKGROUND: In the biological experiments of soybean species, molecular markers are widely used to verify the soybean genome or construct its genetic map. Among a variety of molecular markers, insertions and deletions (InDels) are preferred with the advantages of wide distribution and high density at the whole-genome level. Hence, the problem of detecting InDels based on next-generation sequencing data is of great importance for the design of InDel markers. To tackle it, this paper integrated machine learning techniques with existing software and developed two algorithms for InDel detection, one is the best F-score method (BF-M) and the other is the Support Vector Machine (SVM) method (SVM-M), which is based on the classical SVM model. RESULTS: The experimental results show that the performance of BF-M was promising as indicated by the high precision and recall scores, whereas SVM-M yielded the best performance in terms of recall and F-score. Moreover, based on the InDel markers detected by SVM-M from soybeans that were collected from 56 different regions, highly polymorphic loci were selected to construct an InDel marker database for soybean. CONCLUSIONS: Compared to existing software tools, the two algorithms proposed in this work produced substantially higher precision and recall scores, and remained stable in various types of genomic regions. Moreover, based on SVM-M, we have constructed a database for soybean InDel markers and published it for academic research.

MOTIVATION: Figures and tables in biomedical literature record vast amounts of important experiment results. In scientific papers, for example, quantitative trait locus (QTL) information is usually presented in tables. However, most of the popular text-mining methods focus on extracting knowledge from unstructured free text. As far as we know, there are no published works on mining tables in biomedical literature. In this article, we propose a method to extract QTL information from tables and plain text found in literature. Heterogeneous and complex tables were converted into a structured database, combined with information extracted from plain text. Our method could greatly reduce labor burdens involved with database curation. RESULTS: We applied our method on a soybean QTL database curation, from which 2278 records were extracted from 228 papers with a precision rate of 96.9% and a recall rate of 83.3%, F value for the method is 89.6%.

MOTIVATION: The development of high-throughput sequencing technology has made it possible for more and more researchers to use population sequencing data to mine genes associated with specific traits. However, the massive amounts of sequencing data have also brought new challenges to the researchers. The question of how to browse population genomic data in an easy and intuitive manner must be addressed. Web-based genome browsers allow user to conveniently view the results of genomic analyses, but heavy usage can reduce the response speed of the webpage, which limits its usefulness in the display of large-scale genome data. IndexedDB technology is a good solution to this problem; it supports web browsers and so creates local databases. In this way, data can be read from the local storage, achieving a smooth display of population genomic data. RESULTS: PopGeV has the following characteristics. First, it uses a new encoding method for compression of population SNP and INDEL data. IndexedDB technology is used to download the results to local storage so that users can browse the results smoothly even when the network traffic is heavy. Second, PopGeV identify similar genomic regions between two individuals based on SNP data. Population diversity indexes are calculated when comparing two populations. Third, user defined annotation information can be integrated for user-friendly mining of gene functions. Simulation shows that PopGeV can smoothly display analysis results of population genome containing over 500 individuals with 2 millions SNP data. AVAILABILITY AND IMPLEMENTATION: PopGeV is available at www.soyomics.com/popgev/ CONTACT: [email protected].