Semantic Categories: Uncertainty and Similarity by Ares Fabregat-Hernández, Javier Palanca, Vicent Botti

“…Furthermore, this approach enables the measurement and control of uncertainty in language categorization and the creation of metrics for evaluating semantic similarity. We provide use cases to demonstrate how the proposed methods can be applied and computed, focusing on their interpretability and the universality of categorical constructions. …”
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Semantic memory and creative evaluation by Amit Skurnik, Rakefet Ackerman, Yoed N. Kenett

“…Understanding how knowledge structures relate to metacognitive processes that accompany creative thinking can further elucidate its complexity. Methods Using the relatedness judgment task, we constructed participants’ (N = 106) semantic memory networks. …”
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Ensuring Semantic Consistency in SysML v2 Models Through Metamodel-Driven Validation by Eduardo Cibrian, Jose Olivert-Iserte, Carlos Diez-Fenoy, Roy Mendieta, Juan Llorens, Jose Maria Alvarez-Rodriguez

“…By defining validation rules derived from this metamodel, the method facilitates automated detection of structural and semantic inconsistencies. …”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU). …”
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MSFNet: A Multi-Source Fusion-Based Method with Enhanced Hierarchical Spectral Semantic Perception for Wheat Disease Region Classification by Wenxu Jia, Ziyang Guo, Wenjing Zhang, Haixi Zhang, Bin Liu

“…However, current remote sensing methods often lack hierarchical spectral semantic perception or rely on single-source data and simple fusion, limiting diagnostic performance. …”
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RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion by Shiyan Pang, Weimin Zeng, Yepeng Shi, Zhiqi Zuo, Kejiang Xiao, Yujun Wu

“…To mitigate this challenge, we present RPFusionNet, a novel parallel semantic segmentation framework that is specifically designed to efficiently integrate both local and global features. …”
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LLM-DSK: A Domain-Specific Semantic Knowledge-Guided Ocean Environment Prediction Method Based on Large Language Models by Ning Song, Caichao Lv, Jie Nie, Min Ye, Enyuan Zhao, Jun Ma, Xiong Liu, Zhiqiang Wei

“…Data-driven methods learn patterns of oceanic variable changes directly from data without relying on explicit modeling of complex physical processes based on specific assumptions. …”
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Comprehensive assessment of spatial quality in traditional village landscapes of the Yuanshui River Basin using semantic differential and Entropy Weight Methods by Lie Wang, Ji’an Zhuang, Mo Wang, Mo Wang, Rana Muhammad Adnan, Rana Muhammad Adnan

“…This study proposes a robust framework for evaluating the spatial quality of traditional village landscapes, combining the Semantic Differential (SD) method with the Entropy Weight Method (EWM). …”
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Attention-enhanced residual U-Net: lymph node segmentation method with bimodal MRI images by Juping Qiu, Chunxia Chen, Ming Li, Jingde Hong, Binhua Dong, Shangyue Xu, Yongping Lin

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SegTrackDetect: A window-based framework for tiny object detection via semantic segmentation and tracking by Aleksandra Kos, Karol Majek, Dominik Belter

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Multitask Analysis Method for Tongue Image Based on Edge Computing by Tingting Song, Bin Liu, Fengen Yuan, Yunfeng Wang, Kang Yu, Hao Yang, Qiuyan Li

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Image synthesis method based on multiple text description by NIE Kaiqin, NI Zhengwei

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Semantic Edge Collapse: A Mesh Edge Collapse Algorithm preserving per Face Semantic Information by G. Grzeczkowicz, G. Grzeczkowicz, B. Vallet

“…Using an edge-collapse algorithm, our method combines geometry from an existing mesh with labeled point cloud data to create a continuous mesh with edges aligned to segmentation boundaries, preserving both geometric fidelity and semantic clarity. …”
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TextSCD: Leveraging Text-based Semantic Guidance for Remote Sensing Image Semantic Change Detection by H. Huang, Q. Cheng, Q. Cheng, D. Zhu, X. Huang, Q. Zhao

“…Our approach integrates Gemini to generate change descriptions between bi-temporal images and employs a multi-level semantic extraction method to capture features from both images and their corresponding captions. …”
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Method of Reconstruction of Language Spatial Picture of the World according to Written Monuments of Pre-National Period (on Example of “Arable Land” Field) by L. A. Inyutina, T. S. Shilnikova

“…The novelty of the work is also the development of a method of reconstruction of a semantic field based on the analysis of such texts. …”
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DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images by Zhen Wang, Nan Xu, Zhuhong You, Shanwen Zhang

“…Extensive experiments on the widely used ISPRS 2-D Semantic Labeling dataset and the 15-Class Gaofen Image dataset confirm the superior efficiency of DiffMamba over several state-of-the-art methods.…”
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Encoding Newton’s Alchemical Library: Integrating Traditional Bibliographic and Modern Computational Methods by Meridith Beck Mink, Michelle Dalmau, Wallace Hooper, William R. Newman, James R. Voelkel, John A. Walsh

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A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network by Lihua Bi, Wenjiao Zhang, Xiangfei Zhang, Canlin Li

“…To solve the semantic segmentation problem of night driving-scene images, which often have low brightness, low contrast, and uneven illumination, a nighttime driving-scene segmentation method based on a light-enhanced network was proposed. …”
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Context-aware Location Privacy Protection Method by Haohua Qing, Roliana Ibrahim, Hui Wen Nies

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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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