Software defect prediction often lacks explainable information， such as defect localization， defect explanation， and repair suggestions， making the prediction results difficult to be applied in actual development. To address this problem， an explainable method for constructing a software defect prediction dataset was proposed on the basis of context engineering and Large Language Models （LLMs）， and HandPick， the first multi-programming-language dataset for software defect prediction， was released. First， the TriCogVuln-LLM （LLM-enhanced Triple Cognitive chain for multilingual code Vulnerability mining with common weakness enumeration knowledge） method was designed on the basis of software engineering principles and prior defect knowledge， so as to guide LLMs to complete function description generation， Common Weakness Enumeration （CWE） defect predictions， and repair suggestion generation step by step. Second， a consensus voting mechanism was designed to form an optimal generative model pool for defect prediction， thereby further improving the quality and diversity of the generated data. Finally， the HandPick dataset， covering four mainstream programming languages， was constructed through consensus-driven automated data generation. Downstream task validation results show that the Qwen2.5-14B-HandPick model， fine-tuned on the HandPick dataset， achieves significant improvements compared to baseline models on an independent and public test set， with gains of 19.29， 21.26， 24.11， and 18.30 percentage points in precision， recall， F1-score， and accuracy， respectively. This confirms that the HandPick dataset， generated by the TriCogVuln-LLM method， enhances model's defect identification and analysis capabilities significantly.
Legitimate communicating parties can leverage the randomness of wireless channel state to extract shared key sequences that are information-theoretically secure. To enhance the efficiency of wireless channel key extraction， a difference-based shared key extraction scheme via multi-level quantization was proposed. In the scheme， random modulation was employed to perform high-frequency sampling of the wireless channel， and two quantization algorithms integrated with random sampling difference — Adaptive Symbol Quantization （ASQ） and Balanced Multi-bit Modified Quantization （BMMQ） — were introduced to process the first-order differential sequence， so as to obtain the original key sequence. On this basis， an information negotiation algorithm was applied to correct inconsistent bits in the original key， and the signal was reconstructed using the original key and the first-order differential sequence， and then the signal was requantized， ultimately achieving key synchronization between legitimate communicating parties. Experimental results demonstrate that random sampling difference reduces the correlation coefficient between adjacent sample points to below e?1， thereby decreasing statistical dependence in the key sequence effectively； under a Signal-to-Noise Ratio （SNR） of 25 dB， the ASQ algorithm reduces the Key Disagreement Rate （KDR） to 3.8×10?? while maintaining an Original Key Extraction Rate （OKER） of 0.86； under lossless quantization conditions， the BMMQ algorithm reduces the KDR to 7×10?3. The finally generated shared key sequences pass the NIST （National Institute of Standards and Technology） randomness test， validating the security and effectiveness of the keys.
With the widespread application of software across various domains， software vulnerabilities have shown a continuous upward trend， so that deep learning-based methods for vulnerability detection have gained wide application. However， the existing graph representation learning methods often neglect the influence of edges in the graph on vulnerability detection， and have the representation of edge weights too coarse. To address this issue， a software vulnerability detection method based on edge weight — EWVD （Edge Weight for Vulnerability Detection） was proposed. Firstly， comments， custom variable names， and function names in the source code were cleaned and represented abstractly. Secondly， Sent2Vec was selected to perform embedding representation after comparative analysis. Thirdly， edge weights were calculated comprehensively using three metrics： connection structure， the importance of neighboring nodes， and Jaccard similarity， so as to identify the information transmission capability between nodes. Finally， by leveraging edge weights， perception capability of the model was enhanced for potential relationships between vulnerable statements， thereby determining the importance of edges in the graph. Compared with the best-performing baseline method VulCNN among seven vulnerability detection baseline methods， EWVD achieves an increase of 1.06 percentage points in Accuracy and a decrease of 1.11 percentage points in False Positive Rate （FPR）. It can be seen that EWVD refines the representation of edge weights and improves the overall performance of vulnerability detection.
Panoramic images， due to their unique projection format， suffer from severe geometric distortions. The existing 2D image super-resolution networks fail to account for the geometric distortion characteristics of panoramic images， making them unsuitable for super-resolution reconstruction of such images. Unlike 2D super-resolution networks， panoramic image super-resolution models must focus on the feature differences across different latitude regions and address issues such as insufficient feature capture at different scales and insufficient learning of contextual information. To address the above issues， an Information Compensation-based Panoramic image Super-resolution reconstruction network （ICPSnet） was proposed. Firstly， based on the geometric characteristics of panoramic images， a position awareness mechanism was introduced to calculate the position weight of each pixel in the latitude direction， thereby enhancing the model’s attention to different latitude regions. Secondly， to address the insufficient feature extraction issue at diverse scales， a Cross-Scale Collaborative Attention （CSCA） module was designed， which utilized a multi-kernel convolutional attention mechanism of different receptive fields to obtain rich cross-scale features. Additionally， to improve quality of the reconstructed image， an Information Compensation （IC） block was designed to enhance the network’s ability to learn contextual information by improving the Atrous Spatial Pyramid Pooling （ASPP）. Experimental results on two benchmark datasets， ODI-SR and SUN360， show that when the amplification factor is 4 and 8， ICPSnet improves the Weighted-to-Spherically-uniform Peak Signal-to-Noise Ratio （WS-PSNR） by 0.14 dB， 0.64 dB， and 0.25 dB， 0.26 dB， respectively， compared to current state-of-the-art OSRT （Omnidirectional image Super-Resolution Transformer）. It can be seen that compared to other networks， ICPSnet has superior visual performance with reconstructed images better representing the texture details of high-latitude regions.
The current deep learning based instance segmentation methods cannot fully train the network model and result in sub-optimal segmentation results due to the lack of labeled engine blade data. To improve the precision of aeroengine blade instance segmentation， an aeroengine blade instance segmentation method based on incomplete instance guidance was proposed. Combining with an existing instance segmentation method and an interactive segmentation method， promising aeroengine blade instance segmentation results were obtained. First， a small amount of labeled data was used to train the instance segmentation network， which generated initial instance segmentation results of aeroengine blades. Secondly， the detected single blade instance was divided into foreground and background. By selecting foreground seed points and background seed points， the interactive segmentation method was used to generate complete segmentation results of the blade. After all the blade instances were processed in turn， the final segmentation result of engine blade instance was obtained by merging the results. All the 72 images were used to train the Sparse Instance activation map for real-time instance segmentation （SparseInst）， to produce the initial instance segmentation results. The testing dataset contained 56 images. The mean Average Precision （mAP） of the proposed method is higher than that of SparseInst by 5.1 percentage points. The mAP results of the proposed method are better than those of the state-of-the-art instance segmentation methods， e.g.， MASK R-CNN （Mask Region based Convolutional Neural Network）， YOLACT （You Only Look At CoefficienTs）， BMASK-RCNN （Boundary-preserving MASK R-CNN）.
Concerning the characteristics of breast cancer in Magnetic Resonance Imaging （MRI）， such as different shapes and sizes， and fuzzy boundaries， an algorithm based on multiscale residual U Network （UNet） with attention mechanism was proposed in order to avoid error segmentation and improve segmentation accuracy. Firstly， the multiscale residual units were used to replace two adjacent convolution blocks in the down-sampling process of UNet， so that the network could pay more attention to the difference of shape and size. Then， in the up-sampling stage， layer-crossed attention was used to guide the network to focus on the key regions， avoiding the error segmentation of healthy tissues. Finally， in order to enhance the ability of representing the lesions， the atrous spatial pyramid pooling was introduced as a bridging module to the network. Compared with UNet， the proposed algorithm improved the Dice coefficient， Intersection over Union （IoU）， SPecificity （SP） and ACCuracy （ACC） by 2.26， 2.11， 4.16 and 0.05 percentage points， respectively. The experimental results show that the algorithm can improve the segmentation accuracy of lesions and effectively reduce the false positive rate of imaging diagnosis.
In order to accelerate rendering of 3D scenes by path tracing， a visual?saliency?driven reuse algorithm of indirect lighting in 3D scene rendering was proposed. Firstly， according to the characteristics of visual perception that the regions of interest have high saliency， while other regions have low saliency， a 2D saliency map of the scene image was obtained， which consists of color information， edge information， depth information and motion information of the image. Then， the indirect lighting in the high?saliency area was re?rendered， while the indirect lighting of the previous frame was reused in the low?saliency area under certain conditions， thereby accelerating the rendering. Experimental results show that the global lighting effect of the image generated by this method is real， and the rendering speed of the method is improved in several experimental scenes， and the speed can reach up to 5.89 times of that of the high?quality rendering.
In order to detect the elderly fall high-risk behaviors quickly and accurately on the low-power and low-cost hardware platform， an abnormal behavior detection algorithm based on AlphaPose optimization model was proposed. Firstly， the pedestrian target detection model and pose estimation model were optimized to accelerate the human target detection and pose joint point reasoning. Then， the image coordinate data of human pose joint points were computed rapidly through the optimized AlphaPose model. Finally， the relationship between the head joint point linear velocity and the crotch joint linear velocity at the moment the human body falls was calculated， as well as the change of the angle between the midperpendicular of the torso and X-axis of the image， were calculated to determine the occurrence of the fall. The proposed algorithm was deployed to the Jetson Nano embedded development board， and compared with several main fall detection algorithms based on human pose at present： YOLO （You Only Look Once）v3+Pose， YOLOv4+Pose， YOLOv5+Pose， trt_pose and NanoDet+Pose. Experimental results show that on the used embedded platform when the image resolution is 320×240， the proposed algorithm has the detection frame rate of 8.83 frame/s and the accuracy of 0.913， which are both better than those of the algorithms compared above. The proposed algorithm has relatively high real-time performance and accuracy， and can timely detect the occurrence of the elderly fall behaviors.
Focusing on the problems of the large time consumption of manual detection and the insufficient precision of the current detection methods of elongated pavement distress， a two-stage elongated pavement distress detection method， named Epd RCNN （Elongated pavement distress Region-based Convolutional Neural Network）， which could accurately locate and classify the distress was proposed according to the weak semantic characteristics and abnormal geometric properties of the distress. Firstly， for the weak semantic characteristics of elongated pavement distress， a backbone network that reused low-level features and repeatedly fused the features of different stages was proposed. Secondly， in the training process， the high-quality positive samples for network training were generated by the anchor box mechanism conforming to the geometric property distribution of the distress. Then， the distress bounding boxes were predicted on a single high-resolution feature map， and a parallel cascaded dilated convolution module was used to this feature map to improve its multi-scale feature representation ability. Finally， for different shapes of region proposals， the region proposal features conforming to the distress geometric properties were extracted by the proposal feature improvement module composed of deformable Region of Interest Pooling （RoI Pooling） and spatial attention module. Experimental results show that the proposed method has the mean Average Precision （mAP） of 0.907 on images with sufficient illumination， the mAP of 0.891 on images with illumination problems and the comprehensive mAP of 0.899， indicating that the proposed method has good detection performance and robustness to illumination.
With the existing deep learning algorithms， it is difficult to restore the highly blurred solar speckle images taken by Yunnan Observatories， and it is difficult to reconstruct the high-frequency information of images. In order to solve the problems， a deblurring method for restoring the solar speckle images and recovering the high-frequency information of images based on Generative Adversarial Network （GAN） and gradient information was proposed. The proposed method was consisted of one generator and two discriminators. Firstly， the image multi-scale features were obtained by the generator with the Feature Pyramid Network （FPN） framework， and these features were input into the gradient branch hierarchically to capture the smaller details in the form of gradient map， and the solar speckle image with high-frequency information was reconstructed by combining the gradient branch results and the FPN results. Then， based on the conventional adversarial discriminator， another discriminator was added to ensure the gradient map generated by the gradient branch more realistic. Finally， a joint training loss including pixel content loss， perceptual loss and adversarial loss was introduced to guide the model to perform high-resolution reconstruction of solar speckle images. Experimental results show that， compared with the existing deep learning deblurring method， the proposed method with image preprocessing has stronger ability to recover the high-frequency information， and significantly improves the Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） indicators， reaching 27.801 0 dB and 0.851 0 respectively. The proposed method can meet the needs for high-resolution reconstruction of solar observation images.
Concerning the problems of complicated structure and revolution of 2D traditional neighborhood cellular automata, low encrypting efficiency, little key space of 1D cellular automata, low diffusion speed and needing multiple rounds iteration to produce avalanche effect, a new encryption algorithm based on 2D X-type reversible cellular automata and Arnold transformation was proposed. Firstly, the plaintext was evolved by the proposed cellular automata, then it was transformed by Arnold transformation and cyclic shift transformation after every evolution, until the ciphertext was encrypted well enough. The experimental result shows that the key space is increased by 16.8% and has perfect robustness in resisting brute force attack. In addition the diffusion and confusion is so excellent that it can produce higher avalanche effect and resist chosen plaintext attack.
