CASSC: Context‐aware method for depth guided semantic scene completion by Jinghao Cao, Ming Li, Sheng Liu, Yang Li, Sidan Du

“…This paper presents CASSC, a novel adaptive context‐aware method based on Transformer networks, aimed at realizing camera‐based semantic scene completion algorithms. …”
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A dual attention mechanism semantic segmentation method for autonomous driving by WANG Yannian, RUAN Pei, LIAN Jihong, ZHENG Fangliang

“…Experimental findings substantiate that this enhanced network model significantly elevates the accuracy of semantic segmentation. It achieves an average intersection over union (mIoU) of 80.4% on the Cityscapes dataset, marking a substantial improvement of 10.4% in comparison to the baseline fully convolutional network (FCN) method.…”
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Real-time Semantic Segmentation Method Based on Improved Feature Fusion by WANG Xiao yu, LI Zhi bin

“… Aiming at the problem that both location information and semantic information need to be considered in real-time semantic segmentation tasks, we proposed a real-time semantic segmentation method based on improved feature fusion. …”
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Improved U-Net++ Semantic Segmentation Method for Remote Sensing Images by Yang Xu, Bin Cao, Hui Lu

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Ontology-based semantic data interestingness using BERT models by Abhilash CB, Kavi Mahesh, Nihar Sanda

Subjects: “…semantic data mining…”
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Research on Lightweighting Methods for 3D Building Models Based on Semantic Constraints by J. Wu, T. Shen, L. Huo

“…To address this problem, this paper proposes a lightweighting method for 3D building models based on semantic constraints. …”
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Retracted: Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control by Journal of Control Science and Engineering

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An integrated graph-spatial method for high-performance geospatial-temporal semantic query by Zichen Yue, Wei Zhu, Xin Mei, Shaobo Zhong

“…To address this issue, we propose GraST, a geospatial-temporal semantic query optimization method that integrates property graphs and relational databases. …”
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The semantic method for agents’ knowledge representation in the Cognitive Integrated Management Information System by Marcin Hernes

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Matching Method for Power Grid Fault Handling Plan Based on Semantic Enhancement by Fei MENG, Jiangpeng LI, Tao LI, Jianzhong XU, Haiyang GAO, Yongtian QIAO

“…In order to improve the matching efficiency and accuracy of grid fault handling plan, a semantic enhancement-based grid fault handling plan matching method is proposed. …”
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Method-Level Syntactic and Semantic Clustering for Microservice Discovery in Legacy Enterprise Systems by Adambarage Anuruddha Chathuranga de Alwis, Alistair Barros, Colin Fidge, Artem Polyvyanyy

“…Our remodularization technique uses both semantic properties of enterprise systems, i.e., domain-level business object and method relationships, together with syntactic features of the methods’ code, e.g., their call patterns and structural similarity. …”
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Intelligent Casting Quality Inspection Method Integrating Anomaly Detection and Semantic Segmentation by Min-Chieh Chen, Shih-Yu Yen, Yue-Feng Lin, Ming-Yi Tsai, Ting-Hsueh Chuang

“…In actual practice, we found that an insufficient workforce limits traditional manual inspection methods and often creates difficulty in unifying quality judgment standards. …”
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SNMatch: An Unsupervised Method for Column Semantic-Type Detection Based on Siamese Network by Tiezheng Nie, Hanyu Mao, Aolin Liu, Xuliang Wang, Derong Shen, Yue Kou

“…Unlike traditional methods, which typically rely on keyword matching or supervised classification, SNMatch leverages unsupervised learning to tackle the challenges of column semantic detection in massive datasets with limited labeled examples. …”
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A Semantic Segmentation-Based GNSS Signal Occlusion Detection and Optimization Method by Zhe Yue, Chenchen Sun, Xuerong Zhang, Chengkai Tang, Yuting Gao, Kezhao Li

“…To address this issue, we dig into the environmental perception perspective to propose a semantic segmentation-based GNSS signal occlusion detection and optimization method. …”
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Information Retrieval Method for the Qur’an based on FastText and Latent Semantic Indexing by aziz ramadhan, Fandy Setyo Utomo

“…Retrieving contextually relevant verses from the Al-Qur'an translation dataset presents significant challenges due to the linguistic richness and semantic variation of the text. This study aims to enhance the accuracy and relevance of information retrieval in the Al-Qur'an translation dataset by combining Latent Semantic Indexing (LSI) and FastText word embeddings. …”
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Research on the aesthetic sensitivity evaluation of tourism mascots based on semantic differential method. by Jing Wang, Wenting Yao, Sheng Su, Jing Zhang, Li Wang

“…Subsequently, tourists who came to Xi'an were invited to participate in a satisfaction survey. Using the semantic differential methods, representative adjective pairs were selected from five dimensions: market positioning, market trends, color decoration, stylistic features, and psychological perception as emotional evaluation criteria. …”
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Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases by Deyan Chen, Hong Zhao, Xia Zhang

“…In this paper, an aided diagnosis method based on domain semantic knowledge bases is proposed. …”
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Sarcasm detection method based on fusion of text semantics and social behavior information by Zhaoyang FU, Zhikai CHEN, Li PAN

“…Sarcasm is a complex implicit emotion that poses a significant challenge in sentiment analysis, particularly in social network sentiment analysis.Effective sarcasm detection holds immense practical significance in the analysis of network public opinion.The contradictory nature of sarcastic texts, which exhibit implicit semantics opposite to the real emotions of users, often leads to misclassification by traditional sentiment analysis methods.Moreover, sarcasm in daily communication is often conveyed through non-textual cues such as intonation and demeanor.Consequently, sarcasm detection methods solely relying on text semantics fail to incorporate non-textual information, thereby limiting their effectiveness.To leverage the power of text semantics and social behavior information, a sarcasm text detection method based on heterogeneous graph information fusion was proposed.The approach involved the construction of a heterogeneous information network encompassing users, texts, and emotional words.A graph neural network model was then designed to handle the representations of the heterogeneous graph.The model employed a dual-channel attention mechanism to extract social behavior information, captured the deep semantics of text through emotional subgraphs, and ultimately combined text semantics and social behavior information.Extensive experiments conducted on the Twitter dataset demonstrate the superiority of the proposed method over existing approaches for sarcasm text detection and classification.…”
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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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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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