Explicit and Implicit Feature Contrastive Learning Model for Knowledge Graph Link Prediction by Xu Yuan, Weihe Wang, Buyun Gao, Liang Zhao, Ruixin Ma, Feng Ding

“…Meanwhile, the subgraph mechanism is introduced to preserve the structural information of explicitly connected entities. Implicit semantic features and explicit structural features serve as complementary information to provide high-quality self-supervised signals. …”
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Aggregating multi-scale contextual features from multiple stages for semantic image segmentation by Dingchao Jiang, Hua Qu, Jihong Zhao, Jianlong Zhao, Meng-Yen Hsieh

“…In this paper, we propose a multi-scale context U-net (MSCU-net) for semantic image segmentation. This network uses a multi-scale context block (MSCB) to aggregate multi-level features and employs the CRF layer to explicitly model the dependencies among pixels. …”
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Dual-Branch Occlusion-Aware Semantic Part-Features Extraction Network for Occluded Person Re-Identification by Bo Sun, Yulong Zhang, Jianan Wang, Chunmao Jiang

“…In the first branch, we introduce an Occlusion-Aware Semantic Attention (OASA) module to extract semantic part features, incorporating a parallel channel and spatial attention (PCSA) block to precisely distinguish between pedestrian body regions and occlusion noise. …”
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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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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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SCATrans: semantic cross-attention transformer for drug–drug interaction predication through multimodal biomedical data by Shanwen Zhang, Changqing Yu, Chuanlei Zhang

“…In the model, BioBERT, Doc2Vec and graph convolutional network are utilized to embed the multimodal biomedical data into vector representation, BiGRU is adopted to capture contextual dependencies in both forward and backward directions, Cross-Attention is employed to integrate the extracted features and explicitly model dependencies between them, and a feature-joint classifier is adopted to implement DDI predication (DDIP). …”
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A diagnostic system for detecting COVID-19 patients depending on lexicon semantic and Biterm Topic Model-Based Feature Selection on WhatsApp Messages Classification by Raghad Hatem, Noor Hussein Eliwe

“…The system processes WhatsApp messages through multiple stages: initial data collection, Word2Vec embedding, lexicon semantic enhancement, vector-space model creation, Biterm Topic Model-based feature selection, and finally, Naive Bayes classification. …”
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Dual Branch Graph Representation Learning-Based Approach for Next Point-of-Interest Recommendation by Guoning Lv, Min Gao

“…Subsequently, it constructs a semantic association graph which preserves the semantic relations between POIs and are further fed into a graph neural network-based backbone to learn the representations of POIs in the semantic feature space. …”
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Structural Types of Speech Acts with the Explicit Mode of Belief and their Expressive Properties in Scientific Dialogue by O. N. Chalova

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Semantic surprise predicts the N400 brain potential by Alma Lindborg, Lea Musiolek, Dirk Ostwald, Milena Rabovsky

“…Here, we test one of the recently proposed, computationally explicit hypotheses on the N400 – namely, that it reflects surprise with respect to a probabilistic representation of the semantic features of the current stimulus in a given context. …”
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Dialogue in In-Depth Cognition of the Subject’s Psyche: Functioning of Pragmatic Referent Statements by Тамара Яценко, Ернест Івашкевич, Любов Галушко, Лариса Кулакова

“…Pragmatic-explicit reference statements have all the features of performatives and can be fully characterized as pragmatic performatives. …”
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A Deep Paraphrase Identification Model Interacting Semantics with Syntax by Leilei Kong, Zhongyuan Han, Yong Han, Haoliang Qi

“…DPIM-ISS introduces the linguistic features manifested in syntactic features to produce more explicit structures and encodes the semantic representation of sentence on different syntactic structures by means of interacting semantics with syntax. …”
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An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication by Shuhui Wang, Zuxing Li, Xin Huang, Qi Jiang

“…Semantic communication is emerging as a promising communication paradigm, where semantic coding plays an essential role by explicitly extracting task-critical information. …”
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CONFIXED VERBS WITH NEGATIVE SEMANTICS OF ACTION CONSEQUENCES IN MODERN RUSSIAN by Zara Godizova, Tsyan’ Wang

“…It seems relevant for the study to find out whether the general idea of the negative consequences of action is related to the conixed word-building model, to analyze the interaction of grammatical and lexical semantics of those type of verbs, to determine the features of aspectual situations, to reveal stylistic features of their functioning, to reveal the complexes of meanings expressed by conixed word-building types bearing the negative consequences semantics. …”
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COGNITIVE SEMANTICS OF NOUNS IN DENUMERATIVE WORD FORMATION (BASED ON UKRAINIAN LANGUAGE) by Olha V. Kostryba

“…Revival of interest in the linguistic-cognitive interpretation of the deep level of language, as a means of facilitating the computerization of the semantic continuum of derived words, is driven by the schemati- zation of both implicit and explicit data concerning these words. …”
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Risk assessment of autonomous vehicle based on six-dimensional semantic space by Yanan CHEN, Ang LI, Dan WU

“…To address the problems of inadequate extraction of risk elements and low robustness of risk scenario assessment in autonomous vehicles, a risk assessment framework based on six-dimensional semantic space was proposed, which included risk element extraction based on six-dimensional semantic space and risk scenario assessment based on knowledge graph.Formerly, the semantic space was constructed with RGB and IR data mapped, and rich features were extracted using inter-modal correlations for explicit and potential risk elements.Subsequently, risk elements were distilled into a knowledge graph by semantic role annotation and entity fusion, and an inference method was designed by combining node completion and risk level function for accurate risk assessment.Simulations show that the proposed method surpasses current MSMatch and iSQRT-COV-Net in accuracy, false/missed alarm rate, and processing time.…”
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Risk assessment of autonomous vehicle based on six-dimensional semantic space by Yanan CHEN, Ang LI, Dan WU

“…To address the problems of inadequate extraction of risk elements and low robustness of risk scenario assessment in autonomous vehicles, a risk assessment framework based on six-dimensional semantic space was proposed, which included risk element extraction based on six-dimensional semantic space and risk scenario assessment based on knowledge graph.Formerly, the semantic space was constructed with RGB and IR data mapped, and rich features were extracted using inter-modal correlations for explicit and potential risk elements.Subsequently, risk elements were distilled into a knowledge graph by semantic role annotation and entity fusion, and an inference method was designed by combining node completion and risk level function for accurate risk assessment.Simulations show that the proposed method surpasses current MSMatch and iSQRT-COV-Net in accuracy, false/missed alarm rate, and processing time.…”
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Semantic-Cognitive Study of HOUSE Concept in Vladimir Nabokov’s Novel “Mashen’ka” by M. M. Morarash

“…The author presents a sequential extension of the verbal-semantic component of the analysis from the study of the meaning of the text to knowledge about reality. …”
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FARVNet: A Fast and Accurate Range-View-Based Method for Semantic Segmentation of Point Clouds by Chuang Chen, Lulu Zhao, Wenwu Guo, Xia Yuan, Shihan Tan, Jing Hu, Zhenyuan Yang, Shengjie Wang, Wenyi Ge

“…This paper presents FARVNet, a novel real-time Range-View (RV)-based semantic segmentation framework that explicitly models the intrinsic correlation between intensity features and spatial coordinates to enhance feature representation in point cloud analysis. …”
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Quantum-Inspired Attention-Based Semantic Dependency Fusion Model for Aspect-Based Sentiment Analysis by Chenyang Xu, Xihan Wang, Jiacheng Tang, Yihang Wang, Lianhe Shao, Quanli Gao

“…Inspired by quantum theory, we construct superposition states from text sequences and utilize them with quantum measurements to explicitly capture complex semantic relationships within word sequences. …”
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