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Residual Graph Convolution Collaborative Filtering with Asymmetric neighborhood aggregation

Neural Computing and Applications 2024年第22期36卷 13989-14003页

作者： Wang, Tao Qin, Jiwei Ma, Chao School of Computer Science and Technology Xinjiang University Urumqi830046 China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830046 China

Due to the superior performance of graph convolutional networks (GCNs) in feature extraction and representation, researchers have introduced GCNs to collaborative filtering (CF) to improve the accuracy of recommendations. However, existing GCN-based CF models still have three shortcomings: degrading the model performance due to symmetric normalization in neighborhood aggregation, underutilizing the node (user or item) representations at lower layers, and failing to alleviate the over-smoothing effect in graph convolution operations. We present a Residual Graph Convolution Collaborative Filtering model with Asymmetric neighborhood aggregation (ARGCCF) to tackle the above shortcomings. Firstly, ARGCCF utilizes asymmetric normalization in neighborhood aggregation, enabling better preservation of high-degree node information for more precisely learning node representations. Secondly, the propagation of ARGCCF embeddings proceeds in two steps. During the initial propagation, a residual connection is established between the node representation of the current layer and the previous layer to utilize the low-level layer’s node representation fully. The embeddings generated after the initial propagation are propagated again in the same graph convolutional layer, which obtains more refined embedding representations to alleviate the over-smoothing problem. Finally, experimentations on five truthful datasets exhibit that ARGCCF has better-improved performance over several mainstream methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Collaborative filtering

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WDE-TransUNet: Brain Tumor Segmentation in MRI Images Utilizing Detail Enhancement and Transformers 24

WDE-TransUNet: Brain Tumor Segmentation in MRI Images Utiliz...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Ni, Xinhu Mo, Jiaqing Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang Urumqi China

ISBN: (纸本)9798400717826

WDE-TransUNet is a novel neural network architecture specifically designed for robust and precise semantic segmentation in medical imaging. This method addresses the challenges posed by inconsistent image quality, diverse pathologies, and anatomical variations due to different imaging techniques by integrating Transformer and convolutional neural network (CNN) modules within an encoder-decoder framework. It utilizes prior knowledge to assign weights at the input stage, optimizing feature utilization across modalities and mitigating the impact of irrelevant features. The encoder employs detail enhanced convolution (DEConv) and root mean square (RMS)Transformer modules to enhance local feature extraction, and then conveys these features to the decoder via skip connections, enriching both spatial and channel information. Evaluated on the Brain Tumor Segmentation Challenge (BraTS)2019, 2020, and 2021 datasets, this method achieved outstanding performance, with average Dice scores of 84.74%, 86.64%, and 90.66%, respectively. Our experimental results demonstrate that WDE-TransUNet, by integrating RSM Transformer with CNN in an encoder-decoder configuration, is superior to existing algorithms for brain tumor segmentation. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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A small target detection algorithm for aerial image with improved YOLOv5 3

A small target detection algorithm for aerial image with imp...

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3rd International Conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Xue, Xianli Jiao, Weidong Zhang, Siyuan Qu, Wenkang Tianjin Key Laboratory of Intelligent Signal and Lmage Processing Civil Aviation University of China TIANJIN300300 China

ISBN: (纸本)9781510680449

The problem of complex backgrounds, dramatic changes in the scale of objects and the presence of a large number of dense small targets in the UAV aerial images. In this study, a detection algorithm for small targets is proposed based on the YOLOv5s algorithm. Firstly, a new BottleNeck: C3LK module is designed for the C3 module using depthwise separable convolution to construct a large convolutional kernel on the YOLOv5 backbone network, which improves the ability to capture global information and compensates for the lack of local information for detecting small targets. Secondly, a small target detection layer and two feature fusion layers are added. At the same time, In order to better detect objects of different scales in complex backgrounds, a module for multi-scale feature extraction is designed by combining dilated convolution and attention mechanism. The experimental results show that the proposed algorithm improved 5.4% over YOLOv5s in terms of mAP@0.5 and over YOLOv7-tiny. The method meet the requirement of real-time detection in aerial image small target detection, and improve the detection performance in small target scenes. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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An Improved Compressive Sensing-Based Model for Efficient Analysis of Monostatic Electromagnetic Scattering Problems

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 388-391页

作者： Qi, Qi Fan, Yunuo Cao, Xinyuan Liu, Yi Kong, Meng Huang, Zhixiang Wu, Xianliang Hefei Normal University Anhui Province Key Laboratory of Simulation Calculation and Design for Electronic Information System Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

To further accelerate the analysis of monostatic electromagnetic (EM) scattering problems of objects, an improved compressive sensing (CS)-based model is proposed. First, an orthogonal subspace spanned by the wide-angle induced currents is identified by means of the wide-angle incident sources. Then, the sparse transform of the induced currents is replaced by the subspace projection. In addition, an optimal number of measurements is determined, only slightly larger than the dimension of the subspace. Finally, the recovery algorithm is simplified to the least-squares method to solve the resulting overdetermined system. Compared with the traditional CS-based model, the proposed model significantly improves the efficiency of the recovery step and optimizes both the requirement for and the way of determining the number of measurements. © 2023 IEEE.

关键词： Least squares approximations

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Classification of Cervical Images Based On Deep Neural Network With Attention Mechanism 6

Classification of Cervical Images Based On Deep Neural Netwo...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Feng, Yun Chen, Lan Zhang, Xia Li, Yingsong Li, Liping Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

In this paper, we propose a DenseNet-CBAM model that utilizes DenseNet121 as the backbone network for feature extraction. The features are then weighted using the Convolutional Block Attention Module (CBAM), which incorporates both channel and spatial attention mechanisms. The features are further processed with ReLU activation and adaptive average pooling operations. Finally, a linear classifier is applied to output the final category predictions. Experiment results show that the proposed model performs the best in terms of the ability in detecting patients with high-grade squamous intraepithelial lesions (HSIL). © 2023 IEEE.

关键词： Image classification

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A Novel Stacked Network Method for Enhancing the Performance of Side-Channel Attacks

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computers, Materials & Continua 2025年第4期83卷 1001-1022页

作者： Zhicheng Yin Lang Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangNormal UniversityHengyang421002China

The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure *** previous studies have applied this method to break targets protected with *** the increasing number of studies,the problem of model *** research mainly focuses on exploring hyperparameters and network architectures,while offering limited insights into the effects of external factors on side-channel attacks,such as the number and type of *** paper proposes a Side-channel Analysis method based on a Stacking ensemble,called *** our method,multiple models are deeply *** the extended application of base models and the meta-model,Stacking-SCA effectively improves the output class probabilities of the model,leading to better ***,this method shows that the attack performance is sensitive to changes in the number of ***,five independent subsets are extracted from the original ASCAD database as multi-segment datasets,which are mutually *** method shows how these subsets are used as inputs for Stacking-SCA to enhance its attack *** experimental results show that Stacking-SCA outperforms the current state-of-the-art results on several considered datasets,significantly reducing the number of attack traces required to achieve a guessing entropy of ***,different hyperparameter sizes are adjusted to further validate the robustness of the method.

关键词： Side-channel analysis deep learning stacking ensemble learning model generalization

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A Band-pass FSS with Wide Angular Stability and High Polarization Stability 6

A Band-pass FSS with Wide Angular Stability and High Polariz...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Gao, Yunshuai Ding, Shuying Huang, Zhixiang Li, Yingsong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

A mimic multi-layer polarization-independent FSS with accessible angular-stability is reported using a three-layer structure, where the top layer and the bottom layer are realized based the metal rings that are same, and the middle is constructed by a slotted metal structure. The mentioned three layers are aimed to separate the used two dielectric substrates. For a well illustration, the constructed dimensions of the installed FSS are 5× 5 mm2. As a case of the FSS cell design, here the dimension is limited as 0.15λ× 0.15λ. Since two different structure is used, the FSS has double resonace, making a wide operation. From the numerical evaluations, the FSS can reject the signal from C-and X-band. © 2023 IEEE.

关键词： Polarization

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A RIS-Supported DOA Estimation Using Combined Norm Constraint in Non-Gaussian Noise Environments 6

A RIS-Supported DOA Estimation Using Combined Norm Constrain...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： He, Lei Li, Liping Ying, Run Yu, Canping Li, Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

Reconfigurable intelligent Surface (RIS) is a revolutionary technology in modern wireless communication systems, since it can adjust the wireless communication environment with passive components. In this paper, we consider the system environment in the presence of Base Station (BS) interference and implement the estimation of the target Direction-of-Arrival (DOA). To improve the estimation performance, we propose a DOA estimation algorithm based on RIS. The proposed algorithm consists of an error term for the lorentzian bound function and a regular term for the atomic norm (termed as LBF-AN), which are implemented to eliminate system noise and signal interference, respectively. The simulation results show that the DOA estimation performance of the proposed algorithm is better than existing algorithms. © 2023 IEEE.

关键词： Gaussian noise (electronic)

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A Compact Double-Layer Wide Beam Circularly Polarized Beidou Antenna 6

A Compact Double-Layer Wide Beam Circularly Polarized Beidou...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Meng, Lulu Min, Sipei Huang, Zhixiang Li, Yingsong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

This paper proposes and designs a microstrip antenna that can work in the B1 band for Beidou with a double-layer substrate, and realizes the circularly polarized radiation characteristics. The antenna consists of a ground plane, two substrates, and two metal patches. The first layer of the patch is a rectangular structure as well as the second layer of the patch is a T-shaped structure with two vertical branches which are to achieve circular polarization. This structure adopts a simple coaxial feeding method, with the probe passing through the second layer of the substrate to connect the ground plane and the T-shaped strip. The simulation optimization results using the HFSS 2021 show that the axial ratio (AR) beam width in both planes exceeds 160°, and the AR at the design frequency is 0.18 dB. The impedance bandwidth is 70 MHz, which can meet the requirements of Beidou's operation. © 2023 IEEE.

关键词： Microstrip antennas

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