Combining deep learning and bird sound recognition strongly supports monitoring bird species and maintaining ecological balance. However, in outdoor environments, the extraction of bird sound features is often hindered by environmental noise, making it challenging for models to learn the fine-grained features of bird sounds fully. And single-scale feature extraction is harrowing to cover the time–frequency domain feature information of bird sounds in multiple dimensions. To address these issues, this paper proposes a multi-grained detail-enhanced and patch-aware network. The model utilizes densely connected time delay neural network as the backbone network and introduces the multi-grained detail-enhanced convolution, which combines vanilla convolutions with differential convolutions in the horizontal, vertical, angular, and central levels, and incorporates multi-grained pooling strategies to learn fine-grained acoustic features at different levels. To further overcome the limitations of single-scale feature extraction, the branch patch-aware attention module is proposed. This module collaboratively captures local details and global contextual information through a multi-branch structure and patch partitioning of different sizes. On the three datasets, the method achieved accuracies of 96.29%, 86.51%, and 97.40%, respectively. This achievement demonstrates the precise capture and parsing ability of the method for audio feature information.

In computer vision, the intersection determination of polygonal areas is utilized to segment different regions in an image and assist in detecting the boundaries of the regions. Moreover, the secure computation of the intersection area of polygons can solve the private calculation of geometric problems in machine learning. A security protocol under the semi-honest model was designed for the problem of secure computation of the intersection area of two polygons. This protocol adopts a new coding method and the Paillier homomorphic encryption algorithm. Aiming at the malicious behaviors that malicious participants may carry out in the semi-honest protocol, a secure computation protocol for the intersection area of polygons under the malicious model was designed by using methods such as hash function, cut-and-choose and zero-knowledge proof. The security of this protocol was proved, and its computational complexity and communication complexity were analyzed. Compared with the existing schemes, it is more efficient.

ABSTRACT Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physicians often face challenges in diagnosing this condition. Therefore, a novel adaptive multi‐channel fusion network (AMCF‐Net) is proposed for computer‐aided diagnosis of lung nodules. First, a Multi‐Channel Fusion Model module is designed, which divides the channels into two parts in specific proportions, effectively extracting multi‐scale channel information while reducing network parameters. After the feature maps output at each layer of the AMCF‐Net, a novel adaptive depth‐wise separable convolution with a squeeze‐and‐excitation module is designed to adaptively integrate the feature maps of various stages of the AMCF‐Net, ensuring that the key lesions of lung nodules are not lost during classification. Finally, a hybrid loss scheme based on an adaptive mixing ratio is proposed to solve the problem of an imbalanced number of positive and negative nodule samples in the dataset. The model achieved the following test results: an accuracy of 90.22%, a specificity of 98.19%, an F1‐score of 86.57%, a sensitivity of 86.49%, and a G‐mean of 87.72%. Compared with other advanced networks, AMCF‐net delivers high‐precision lung nodule classification with minimal inference cost. Related codes have been released at: https://github.com/GuYuIMUST/AMCF‐net .

With the wide applications of wireless sensor networks (WSN) in the fields of smart transportation and industrial internet of things (IIoT), there is an increasing demand for their security and trustworthiness. To solve the problem of WSN’s malicious nodes such as identity forgery attacks, node spoofing, and man-in-the-middle attacks, this paper proposes a scheme that detects malicious nodes by securely computing spatially parallel straight-line distance (SPSLD) and combining it with secure multi-party computation (MPC). This scheme uses the NTRU encryption algorithm with the additive homomorphism to design the SPSLD secure computation protocol under the semi-honest model, and for the malicious attack behaviors present in it, the secure protocol under the malicious model is proposed with the cut-and-choose method. The correctness of the protocol under different models is analyzed, and the security is proved by real/ideal model paradigm. Performance comparison and experimental simulation results indicate that, while ensuring security: The computational complexity of the semi-honest model protocol is reduced by at least 85% compared to Paillier-based schemes, with execution time shortened by 32-46%. The malicious model protocol is 12% faster than similar attack-resistant schemes, effectively defending against malicious adversary attacks, although additional overhead is introduced, its execution efficiency remains within an acceptable range for WSN environments, providing an efficient solution for enhancing the security and reliability of WSN.