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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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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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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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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Temporal Features-Fused Vision Retentive Network for Echocardiography Image Segmentation by Zhicheng Lin, Rongpu Cui, Limiao Ning, Jian Peng

“…However, most existing approaches focus on features from ES frames and ED frames, neglecting the inter-frame correlations in unlabeled frames. …”
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Object-Specific Multiview Classification Through View-Compatible Feature Fusion by Javier Perez Soler, Jose-Luis Guardiola, Nicolás García Sastre, Pau Garrigues Carbó, Miguel Sanchis Hernández, Juan-Carlos Perez-Cortes

“…It does not merely use pose as auxiliary data but employs it to align and selectively fuse features from different views. This mathematically explicit fusion of rotations, based on relative poses, allows VCFF to effectively combine multi-view information, enhancing classification accuracy. …”
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MULTIMODAL SYNTACTIC CONSTRUCTIONS: A STRIKING FEATURE OF DIGITAL COMMUNICATION IN MODERN ENGLISH by Larysa L. Makaruk

“…The use of the continuous sampling method made it possible to identify verbal, non-verbal and paraverbal components to be analysed. Structural and semantic analysis were used to identify the semantic and structural features of verbal, non-verbal and paraverbal units, their functional load and their functioning at the syntactic level. …”
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Enhancing Text Classification Through Grammar-Based Feature Engineering and Learning Models by Alaa Mohasseb, Andreas Kanavos, Eslam Amer

“…Grammatical and domain-specific features are explicitly extracted and leveraged to improve multi-class classification. …”
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Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering by Liulong Yao, Jinrong Cui, Yazi Xie, Chengli Sun

“…In addition, existing methods mainly rely on the original graph topology information and fail to fully utilize the neighborhood information hidden in the node attribute features. To address the above problems, we proposes a Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering (NIA-MVFE-CGC) framework, which improves the existing methods from the perspectives of network architecture, feature redundancy and neighborhood information. …”
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Identity Hides in Darkness: Learning Feature Discovery Transformer for Nighttime Person Re-Identification by Xin Yuan, Ying He, Guozhu Hao

“…To this end, we propose a novel nighttime person Re-ID method, termed Feature Discovery Transformer (FDT), explicitly capturing the pedestrian identity information hidden in darkness at night. …”
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AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features by Ruochen Zhang, Hyeung-Sik Choi, Dongwook Jung, Phan Huy Nam Anh, Sang-Ki Jeong, Zihao Zhu

“…AuxDepthNet introduces two key components: the Auxiliary Depth Feature (ADF) module, which implicitly learns depth-sensitive features to improve spatial reasoning and computational efficiency, and the Depth Position Mapping (DPM) module, which embeds depth positional information directly into the detection process to enable accurate object localization and 3D bounding box regression. …”
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Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning by Cai Chen, Jian Wang, Dong Li, Xiaohu Sun, Jiyong Zhang, Changjiang Yang, Bo Zhang

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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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