Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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A Semantic Segmentation Method for Winter Wheat in North China Based on Improved HRNet by Chunshan Wang, Penglei Zhu, Shuo Yang, Lijie Zhang

“…Compared to traditional methods, our model demonstrated better segmentation performance in winter wheat semantic segmentation tasks. …”
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Face Detection Method based on Lightweight Network and Weak Semantic Segmentation Attention Mechanism by Xiaoyan Wu

“…A face detection method based on lightweight network and weak semantic segmentation attention mechanism is proposed in this paper, aiming at the problems of low detection accuracy and slow detection speed in face detection in complex scenes. …”
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THE PRINCIPLES AND METHODS OF INFORMATION AND EDUCATIONAL SPACE SEMANTIC STRUCTURING BASED ON ONTOLOGIC APPROACH REALIZATION by Yurij F. Telnov

“…This article reveals principles of semantic structuring of information and educational space of objects of knowledge and scientific and educational services with use of methods of ontologic engineering. …”
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Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings by Pengyu Zhu, Youwei Li, Peidong Xu, Ping Li, Zhenbing Zhao, Gang Li

“…Finally, the model employs a hybrid similarity measurement mechanism that comprehensively considers both semantic and structural features, combining K-means clustering similarity search with a multi-node weighted evaluation method to achieve efficient and accurate risk assessment. …”
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A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses by Pengpeng Li, Qing Zhu, Jiping Liu, Tao Liu, Ping Du, Shuangtong Liu, Yuting Zhang

“…This paper proposes a multi-semantic feature fusion method for complex address matching of Chinese addresses that formulates address matching as a classification task that directly predicts whether two addresses refer to the same location, without relying on predefined similarity thresholds. …”
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Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM by Zhanrong Li, Chao Jiang, Yu Sun, Haosheng Su, Longning He

“…The proposed method first identifies outlier feature points through rigorous geometric consistency checks, then employs morphological dilation to expand the initially detected dynamic regions. …”
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A method for feature division of Soccer Foul actions based on salience image semantics by Jianming Wang, Lifeng Li

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Building extraction method for aerial images based on DeepLabv3+ semantic segmentation by LIAO Yuanhui, WANG Jingdong, LI Haoran, YANG Heng

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A Multi-Branch Attention Fusion Method for Semantic Segmentation of Remote Sensing Images by Kaibo Li, Zhenping Qiang, Hong Lin, Xiaorui Wang

“…To address these challenges in remote sensing image semantic segmentation, we propose a highly generalizable multi-branch attention fusion method based on shallow and deep features. …”
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A Semantically Enhanced Label Prediction Method for Imbalanced POI Data Category Distribution by Hongwei Zhang, Qingyun Du, Shuai Zhang, Renfei Yang

“…This result surpasses the performance of traditional methods, highlighting the effectiveness of the proposed method.…”
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A method for feature division of Soccer Foul actions based on salience image semantics. by Jianming Wang, Lifeng Li

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SNN-Based Semantic Segmentation Method Using Adaptive Threshold and Multi-Feature Fusion by HUANG Yongbin, LI Chen, DONG Wenbo, LIU Shunlian

“…Additionally, the proposed method consumed less energy compared to ANN-based semantic segmentation algorithms.…”
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A Localization Method for UAV Aerial Images Based on Semantic Topological Feature Matching by Jing He, Qian Wu

“…In order to address the problem of Unmanned Aerial Vehicles (UAVs) being difficult to locate in environments without Global Navigation Satellite System (GNSS) signals or with weak signals, this paper proposes a localization method for UAV aerial images based on semantic topological feature matching. …”
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Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion by Chenghao Zhang, Lingfei Wang, Chunyu Zhang, Yu Zhang, Peng Wang, Jin Li

“…To address these challenges, this study proposes an improved U-Net-based liver semantic segmentation method that enhances segmentation performance through optimized feature extraction and fusion mechanisms. …”
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Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution by Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

“…However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. …”
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Novel channel attention-based filter pruning methods for low-complexity semantic segmentation models by Md. Bipul Hossain, Na Gong, Mohamed Shaban

“…While the aforementioned models have been deemed very successful in segmenting medical targets including organs and diseases in high resolution images, the computational complexity represents a burden for the real-time application of the algorithms or the deployment of the models on resource-constrained platforms. Until recently, few methods have been introduced for optimizing or pruning of the parameters of the semantic segmentation models. …”
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Improved Indoor 3D Point Cloud Semantic Segmentation Method Based on PointNet++ by Xingfei Tan, Yonghua Xia, Bin Wang, Ruidong Zou

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Possibilities of Searching for Linguistic Means of Expressing Semantic Categories in Translation by Using Corpus-Based Methods by I. A. Lekomtseva, A. Kh. Abdulmanova, M. N. Kulikova

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Intrusion detection method based on hierarchical hidden Markov model and variable-length semantic pattern by DUAN Xue-tao1, JIA Chun-fu 1, LIU Chun-bo1

“…The defects of intrusion detection using fixed-length short system call sequences were analyzed. A method of extracting variable-length short system call sequences, grounded on the function return addresses stored in the process stacks, was proposed. …”
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