Backdoor defense method in federated learning based on contrastive training by Jiale ZHANG, Chengcheng ZHU, Xiang CHENG, Xiaobing SUN, Bing CHEN

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Learning Part-Based Features for Vehicle Re-Identification with Global Context by Rajsekhar Kumar Nath, Debjani Mitra

“…We propose a novel part-based model that unifies a global component by taking the distances of the parts from the global feature vector and using them as loss weights during the training of the individual parts, without increasing complexity. …”
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Implementation of project-based training in the educational process of a university by Natalya Mikhailovna Suetina, Azamat Kaplanovich Temzokov

“…The article is devoted to studying the features of the implementation of project-based learning technology in the educational process of a university. …”
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SIDH: A Game-Based Architecture for a Training Simulator by P. Backlund, H. Engström, M. Gustavsson, M. Johannesson, M. Lebram, E. Sjörs

“…These features are expected to enhance immersion and fidelity. …”
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Research on Sports Training Action Image Recognition Based on SDN by Dianhai Wang, Lianmei Shen

“…Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. …”
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ENGINEERING PEDAGOGY AS A BASE FOR TECHNICAL TEACHER TRAINING SYSTEM by V. V. KONDRATYEV

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Chinese Sequence Labeling Based on Stack Pre-training Model by LIU Yu-peng, LI Guo-dong

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Physical function evaluation in volleyball training based on intelligent GRNN by Kaiyuan Dong, Borhannudin bin Abdullah, Hazizi bin Abu Saad, Chenxi Lu

“…Abstract This study aims to improve both the evaluation accuracy and the real-time feedback capability in monitoring athletes’ physical function changes during volleyball training. Firstly, based on the framework of the generalized regression neural network, a variable-structure generalized regression neural network (VSGRNN) is proposed and developed. …”
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An interactive ball training partner robot based on YOLOv5 by Zeng Yangji, Liu Zihong, Cai Yong, Guo Xingchen, Mo Jinlong

“…Compared with other algorithms based on feature matching and small target detection using gestures, the robot has higher detection speed and accuracy, which makes the robot more intelligent.…”
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Visualization of simulation-based training in otolaryngology: A bibliometric analysis by Wenqi Yang, Xiaowei Zhang, Yuzhe Kong, Jiayin Li, Ru Gao, Xiaohong Tang

“…Objectives: This study aims to comprehensively summarize the advancements and insights in simulation-based otolaryngology training, highlighting the growth trends, geographical distribution, institutional contributions, journal impacts, authorship patterns, reference co-citations, and keyword dynamics over the past two decades. …”
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Intelligent classification models for food products basis on morphological, colour and texture features by Narendra Veernagouda Ganganagowder, Priya Kamath

“…., using digital images. The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features are used to train the models for classification and detection. …”
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Adaptive Track Association Method Based on Automatic Feature Extraction by Zhaoyue Zhang, Guanting Dong, Chenghao Huang

“…To achieve accurate identification and association between radar tracks and ADS-B tracks, this study proposes an adaptive feature extraction method based on the longest common subsequence (LCSS) combined with classification theory to address the limitations inherent in traditional machine learning-based track association approaches. …”
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Recognition of PQ stego images based on identifiable statistical feature by Ji-cang LU, Fen-lin LIU, Xiang-yang LUO, Yi ZHANG

“…A PQ (perturbed quantization) stego images recognition algorithm is proposed based on identifiable statistical feature. According to the specific changing ways of PQ steganography to image data, the proposed algorithm extracts the identifiable statistical feature that can distinguish PQ stego images from other types of stego images. …”
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Multi-adversarial domain adaptation method based on feature correction by Yong ZHANG, Haoshuang LIU, Qi ZHANG, Wenzhe LIU

“…Domain adaptation can transfer labeled source domain information to an unlabeled but related target domain by aligning the distribution of source domain and target domain.However, most existing methods only align the low-level feature distributions of the source and target domains, failing to capture fine-grained information within the samples.To address this limitation, a feature correction-based multi-adversarial domain adaptation method was proposed.An attention mechanism to highlight transferable regions was introduced in this method and a feature correction module was deployed to align the high-level feature distributions between the two domains, further reducing domain discrepancies.Additionally, to prevent individual classifiers from overfitting their own noisy pseudo-labels,dual classifier co-training was proposed and the feature aggregation property of graph neural networks was utilized to generate more accurate source domain labels.Extensive experiments on three benchmark datasets for transfer learning demonstrate the effectiveness of the proposed method.…”
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Software Defect Prediction Based on Effective Fusion of Multiple Features by Chaozheng Zhang, Junhua Wu

“…Traditional software defect prediction models typically rely on a single feature or perform simple aggregation of multiple features during training. …”
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Multi-Feature Fusion-Based Speech Disorder Classification Using MobileNetV3-EfficientNetB7, Linformer-Performer, and SHAP-Aware XGBoost by Abdul Rahaman Wahab Sait, Suresh Sankaranarayanan, P. Gouthaman

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Learning Feature Fusion in Deep Learning-Based Object Detector by Ehtesham Hassan, Yasser Khalil, Imtiaz Ahmad

“…In this work, we present an object detection framework by learning-based fusion of handcrafted features with deep features. …”
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Virtual Reality Video Image Classification Based on Texture Features by Guofang Qin, Guoliang Qin

“…Therefore, this article proposes a dense convolutional neural network classification algorithm based on texture features for images in virtual reality videos. …”
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Cattle identification based on multiple feature decision layer fusion by Dongxu Li, Baoshan Li, Qi Li, Yueming Wang, Mei Yang, Mingshuo Han

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Multi-Feature Facial Complexion Classification Algorithms Based on CNN by Xiyuan Cao, Delong Zhang, Chunyang Jin, Zhidong Zhang, Chenyang Xue

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