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JGC-IAGCL: Fusing joint graph convolution and intent-aware graph contrastive learning for explainable recommendation

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Information Fusion 2025年 123卷

作者： Yang, Zhi Lin, Chuan Qin, Yongbin Huang, Ruizhang Chen, Yanping Qin, Jiwei State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang Guizhou550025 China College of Information Science and Engineering Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

Graph contrastive learning (GCL) enhances recommendation accuracy by leveraging self-supervised features to refine node representations from large-scale unlabeled data. Traditional GCL-based recommendation models typically construct contrastive views via graph augmentation (e.g., stochastic node/edge dropout) or embedding-space perturbation, aiming to maximize representation consistency. However, these methods struggle to effectively model and interpret user preferences and consumption intents, limiting explainability and recommendation performance. To address these challenges, we propose JGC-IAGCL (Joint Graph Convolution and Intent-Aware Graph Contrastive Learning), an explainable recommendation framework. JGC-IAGCL integrates joint graph convolution to capture implicit user preferences and employs intent-aware graph contrastive learning to extract explicit user intents from user–item interactions. By fusing these features, our method generates evenly distributed, intent-propensity-aware user/item representations. Theoretical analysis shows that JGC-IAGCL mitigates popularity bias while enhancing the exposure of long-tail items. Extensive experiments on four highly sparse public datasets validate its effectiveness, demonstrating superior recommendation accuracy and improved interpretability. © 2025

关键词： Self-supervised learning

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Application and visualization of typical clustering algorithms in seismic data analysis

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Procedia Computer Science 2019年 151卷 171-178页

作者： Z. Fan X. Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China

Earthquake brings enormous loss of lives and properties to human beings due to its suddenness, destructiveness and inscrutability. The new techniques for analyzing seismic data can reveal the distribution of earthquakes, which helps us master the laws of earthquake disasters and reduce the risks brought by them. In this paper, we applied K-means and DBSCAN clustering algorithms to the analysis of seismic data. Their performances in fitting seismic belts with seismic datasets are compared. First, we map the positional parameters in the seismic data to coordinate points on a two-dimensional plane and then cluster them with the DBSCAN algorithm. In addition, we combine the magnitude and depth properties of seismic data, use the Elbow method to find the best K value, and then classifies the dataset by K-means algorithm. We visualize the results, and the distinction of each classification is clear. The experimental results show that the DBSCAN algorithm has a better effect on fitting the seismic belt, and the classification results of K-means algorithm for earthquakes are also in line with expectations.

关键词： Seismic Distribution Clustering K-means DBSCAN Visualization

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Manipulating density of magnetic skyrmions via multilayer repetition and thermal annealing

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Physical Review B 2021年第6期104卷 064421-064421页

作者： Xinran Wang Anni Cao Sai Li Jin Tang Ao Du Houyi Cheng Yiming Sun Haifeng Du Xueying Zhang Weisheng Zhao Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao 266000 China Beijing Microelectronics Technology Institute Beijing 100076 China Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory of Chinese Academy of Sciences and University of Science and Technology of China Hefei 230031 China

The magnetic skyrmion, a tiny magnetic texture that holds promise as the next-generation information carrier, has been widely studied in recent years. A fine tunability of skyrmion density is required for its real applications in novel memory and logic devices. Here, we report on the manipulation of skyrmion density at room temperature in a Pt/Co/Ta/MgO system composed of multiple repetitions with perpendicular magnetic anisotropy. Néel-type skyrmions are observed by performing Lorentz transmission electron microscopy and magnetic force microscopy. The influence of structure repetition on skyrmions is experimentally investigated and further understood by micromagnetic simulations. The variation of skyrmion density with repetition number is mainly attributed to magnetic anisotropy. Meanwhile, thermal annealing is utilized to regulate the skyrmion density, where skyrmion-related properties exert a competitive effect. Our findings provide alternative means to manipulate skyrmion density, further allowing for optimized engineering of skyrmion-based devices.

关键词： Magnetic interactions Magnetic texture Skyrmions Spintronics Multilayer thin films Brillouin scattering & spectroscopy Lorentz microscopy Magnetic force microscopy

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Adaptive feature selection with weighted fuzzy rough sets for noisy data

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Fuzzy Sets and Systems 2025年 518卷

作者： Xiaoling Yang Hongmei Chen Tianrui Li Shan Feng Jihong Wan Yiyu Yao School of Mathematical Sciences Sichuan Normal University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University China

The existence of noise in data may decrease the performance of the classification model. An intuitive assumption suggests that samples with a higher local density have a greater probability of being correctly classified. Moreover, there are differences in the importance of features. It should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method. It aims to the impact of noise and obtain the adaptive importance of features. In this paper, the Weighted Fuzzy Rough Sets model (WFRS) is proposed by utilizing more reliable samples to address noisy data. Firstly, the approximation margin is derived by applying the WFRS model within the original feature space from the sample's perspective. It serves as a basis for deriving the feature weights. On this basis, the weighted feature space is obtained. This space compresses the features with the lower weight and stretches the features with higher weight. Then, we obtain a specific fuzzy relation to maximize the within-class samples and minimize the between-class samples in the weighted feature space. Further, an evaluation function of features is constructed to model the uncertainty of fuzzy positive and non-negative regions. A Sample-driven adaptive Feature selection algorithm based on the Weighted fuzzy rough sets model (SFW) is designed to capture the varying importance of features by considering the local density of samples. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of SFW, and its superiority compared to alternative methods.

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Server load prediction algorithm based on CM-MC for cloud systems

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Journal of Systems Engineering and Electronics 2018年第5期29卷 1069-1078页

作者： XU Xiaolong ZHANG Qitong MOU Yiqi LU Xinyuan Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications State Key Laboratory of Information Security Chinese Academy of Sciences Shanghai Stock Exchange Technology Co. LtdShanghai Stock Exchange Institute of Big Data Research at Yancheng Nanjing University of Posts and Telecommunications

Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model(CM) and the Markov chain(MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.

关键词： cloud computing load prediction cloud model Markov chain energy saving

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A Fixed-Time Distributed Algorithm for Least Square Solutions of Linear Equations 31

A Fixed-Time Distributed Algorithm for Least Square Solution...

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第31届中国控制与决策会议

作者： Wu Ai Shuling Li Jian Wu Quanxi Feng College of Science Guilin University of Technology Center for Data analysis and Algorithm Technology Guilin University of Technology School of Computer and Information Anqing Normal University University Key Laboratory of Intelligent Perception and Computing of Anhui Province

ISBN: (纸本)9781728101071;9781728101064

In this paper, we presents a fixed-time convergent distributed algorithm to achieve least square solutions of networked linear equations. Each agent in the network only knows a subset of the equations and can only exchange messages with its nearest neighbors. Compared with the finite-time algorithm, it is shown that the settling time is independent to the initial states of the algorithm and can be preassign according to requirements of the task. A numerical example is provided to illustrate the effectiveness of the theoretical result.

关键词： Fixed-time stability Distributed algorithms Least Squares Linear Equations

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OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area 9th

OSAF_e: One-Stage Anchor Free Object Detection Method Consid...

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Zhang, Yong Zhang, Lizong Rao, Zhihong Duan, Guiduo Wang, Chunyu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China China Electronic Technology Cyber Security Co. Ltd. Chengdu610000 China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9783030596040

The task of object detection is to identify the bounding box of the object and its corresponding category in images. In this paper, we propose a new one-stage anchor free object detection algorithm OSAF_e, with the consideration of effective mapping area. A feature extraction network is used to obtain high level feature, and the true bounding box of the object in the original image is mapped to the grid of feature map, in order to perform category prediction and bounding box regression. The proposed algorithm is evaluated with the Pascal Voc dataset, and the experiments indicate that it has a better result. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Attention-Based Interaction Trajectory Prediction 9th

Attention-Based Interaction Trajectory Prediction

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Liu, Zhe Zhang, Lizong Rao, Zhihong Liu, Guisong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China China Electronic Technology Cyber Security Co. Ltd. Chengdu610000 China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528402 China

ISBN: (纸本)9783030596040

Trajectory prediction is a hot topic in the field of computer vision and has a wide range of applications. Trajectory prediction refers to predicting the future trajectory of a target based on its past trajectory. This paper proposes a method based on graph neural network and attention mechanism, in order to update trajectory characteristics by implement global pedestrian interaction. And, a direct relationship between history and future is introduced with the attention module for reducing error propagation. The method was evaluated on several real-world crowd datasets, the results demonstrate the effectiveness of our method. © 2020, Springer Nature Switzerland AG.

关键词： Trajectories

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Pan-genome and Haplotype Map of Cassava Cultivars and Wild Ancestors Provide Insights into its Adaptive Evolution and Domestication

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Molecular plant 2025年第6期18卷 1047-1071页

作者： Zhiqiang Xia Zhenglin Du Xincheng Zhou Sirong Jiang Tingting Zhu Le Wang Fei Chen Luiz Carvalho Meiling Zou Luis Augusto Becerra López-Lavalle Xiaofei Zhang Liangye Xu Zhenyu Wang Meili Chen Xin Guo Shujuan Wang Mengtao Li Yuanchao Li Haiyan Wang Shisheng Liu Yuting Bao Long Zhao Chenji Zhang Jianjia Xiao Fengguang Guo Xu Shen Haozheng Li Cheng Lu Fei Qiao Hernan Ceballos Huabing Yan Xiaochun Qin Ling Ma Huaifang Zhang Shuang He Wenming Zhao Yinglang Wan Yinhua Chen Dongyi Huang Kaimian Li Bin Liu Ming Peng Weixiong Zhang Birger Lindberg Møller Xin Chen Ming-Cheng Luo Jingfa Xiao Wenquan Wang Hainan University Haikou 570228 China National Key Laboratory of Biotechnology and Breeding of Tropical Crops. National Genomics Data Center Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation Beijing 100101 China. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China. Department of Plant Sciences University of California Davis California 95616 USA. Brazilian Enterprise for Agricultural Research (EMBRAPA) Genetic Resources and Biotechnology Brasilia 70770 Brazil. International Center for Tropical Agriculture (CIAT) Cali 6713 Colombia. Institute of Nanfan & Seed Industry Guangdong Academy of Sciences Guangzhou 510316 China. State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources/College of Life Science and Technology Guangxi University Nanning 530004 China. The Academy of Agriculture and Forestry Sciences Qinghai University Xining 810016 China. Tropical Crop Genetic Resources Institute Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571700 China. Economic Crop Institute Guangxi Academy of Agricultural Sciences Nanning China. School of Biological Science and Technology University of JINAN Jinan China. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China Department of Health Technology and Informatics Department of Computing The Hong Kong Polytechnic University Hong Kong China. Plant Biochemistry Laboratory Department of Plant and Environmental Sciences University of Copenhagen DK-1871 Frederiksberg C Denmark. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China. Electronic address: xiaojingfa@***. National Genomics Data Center Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation Beijing 100101

Cassava is an important, resilient tropical crop that produces large starchy storage roots and high biomass. But how did cassava´s remarkable environmental adaptability and key economic traits evolve from its wild species? We constructed nearly T2T genomes and their haplotype forms for the cultivar AM560, wild ancestors FLA4047 and W14, a graphic pan-genome of 30 representatives with 1.15 Gb, and built a clarified evolutionary tree of 486 accessions. A comparison of structural variations (SVs) and single-nucleotide variations (SNVs) between the ancestors and cultivated cassavas reveals predominant expansions and contractions of genes and gene families, which are mainly driven by transposons. Significant selective sweeping occurred in 122 footprints of genomes and affects 1,519 domesticated genes. We identify selective mutations in MeCSK and MeFNR2 that could promote photoreactions associated with MeNADP-ME in C photosynthesis in modern cassava. Co-evolution of retard floral primordia and initiation of storage roots may arise from MeCOL5 variants with altered bindings to MeFT1, MeFT2, and MeTFL2. Mutations of MeMATE1 and MeGTR occur in sweet cassava, and MeAHL19 has evolved to regulate the biosynthesis, transport, and endogenous remobilization of cyanogenic glucosides in cassava. These extensive genomic and gene resources provided here and the findings of the evolutionary mechanisms responsible for beneficial traits in modern cultivars represent a stepping-stone in future breeding of cassava.

关键词： Cassava Evolution and Domestication Genome Pan-genome Selective sweeping Structural Variations

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Fish vitality assessment under adversity stress based on multi-sensing fusion and deep learning techniques

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Aquaculture 2025年

作者： Yanfei Zhu Wenguan Zhang Yongjun Zhang Xiaoshuan Zhang College of Engineering China Agricultural University Beijing 100083 China College of Engineering Beijing Laboratory of Food Quality and Safety China Agricultural University Beijing China School of Information Engineering Shandong Youth University of Political Science Jinan 250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Jinan 250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan 250103 China

Accurately assessing the vitality of fish during Cryogenic Waterless Live Transportation (CWLT) is crucial for ensuring their survival rate and transport quality. To address this, this study developed a multi-sensing fusion measurement system, integrating the impedance sensor and image sensor to capture fish impedance, phase angle data and visual image data, which can provide reliable data support for vitality assessment. A multi-input deep learning model for cross-modal data fusion, called the whale optimization algorithm-based dual-channel convolutional network (WOA-DConvNet), was developed to assess fish vitality. The study first conducted a correlation analysis between impedance and phase angle data using blood index data, revealing the relationship between different frequency signals and fish vitality status. Cluster analysis and variance analysis were then performed to examine the significant differences in vitality states. The results show significant differences ( P  < 0.05) in impedance and phase angle data between different vitality levels. Finally, the WOA-DConvNet model was used to evaluate the vitality of pearl gentian grouper during the live transport. The results showed that the model achieved excellent performance, achieving precision, recall, f1 score and accuracy of 91.81 %, 91.74 %, 91.76 % and 91.71 %. The core goal of this research is to explore the integrated application of artificial intelligence and advanced sensing technologies in the live transport process, aiming to develop a more precise and intelligent vitality assessment method and promote scientific monitoring and management during live fish transportation.

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