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A knowledge-enhanced interest segment division attention network for click-through rate prediction

Neural computing and Applications 2024年第34期36卷 21817-21837页

作者： Liu, Zhanghui Chen, Shijie Chen, Yuzhong Su, Jieyang Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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The analysis of big data application mode and security risk

The analysis of big Data application mode and security risk

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International Conference on Information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： ZENG Tai-sheng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

ISBN: (纸本)9789462520677

In the current social background,Existing facilities and tools already can not meet the needs of big data in expanding and analysis ***'s data storage and analysis work is achieved under cloud conditions and Hadoop were set *** the conditions for cloud computing,cloud computing applications who have remote data files were not authorized to read its contents,which results in unauthorized manipulation,and it will produce a lot of security risks for large *** this paper,according to the cloud of different modes,Hadoop different stages of the operation,subject to the threat of non- confidence and security to steal big data generated to analyze a variety of privacy,with threat model as an example,it explores ways to address security threats.

关键词： big data security analysis Hadoop

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Design and practice of virtual simulation of industrial Internet 4

Design and practice of virtual simulation of industrial Inte...

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4th International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022

作者： Wu, Qingjin Zeng, Taisheng Zhu, Daxin Cai, Danlin Quanzhou Normal University Laboratory and Equipment Management Office Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou China Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Key Laboratory of Intelligent Computing and Information Quanzhou China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

In the cultivating new engineering talents of information discipline, people tend to adopt virtual simulation to simulate production line scenes of enterprises' intelligent manufacturing so as to solve the problem that students fail to go deep into the production lines of industrial fields for scene practice and that they can not collect, analyze and optimize data in the real intelligent manufacturing production lines. The innovative talent training methods which combine regional industries can be used to improve students' ability to analyze, design, debug, operate, maintain and optimize the intelligent manufacturing big data and to master methods and ideas for solving complex engineering problems with big data technology. They can also provide beneficial experience references for the training of new engineering talents of information discipline. © 2022 IEEE.

关键词： Big data

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Meibomian glands segmentation in infrared images with limited annotation

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International Journal of Ophthalmology(English edition) 2024年第3期17卷 401-407页

作者： Jia-Wen Lin Ling-Jie Lin Feng Lu Tai-Chen Lai Jing Zou Lin-Ling Guo Zhi-Ming Lin Li Li College of Computer and Data Science Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Shengli Clinical Medical College of Fujian Medical University Fuzhou 350001Fujian ProvinceChina Department of Ophthalmology Fujian Provincial Hospital South BranchFujian Provincial HospitalFuzhou 350001Fujian ProvinceChina

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble *** viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,***,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for *** model maintains an exceptionally high level of segmentation accuracy while substantially reducing training *** holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

关键词： infrared meibomian glands images meibomian gland dysfunction meibomian glands segmentation weak supervision scribbled annotation

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Energy-Efficient Virtual Machine Migration Algorithm in data Centers 3

Energy-Efficient Virtual Machine Migration Algorithm in Data...

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3rd International conference on Computer, Big data and Artificial Intelligence, ICCBDAI 2022

作者： Wu, Xiaodong Zeng, Taisheng School of Mathmatics and Computer Science Quanzhou Normal University Fujian Province Quanzhou362000 China Fujian Provincial Key Laboratory of Intensive Computing Fujian Province Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Province Quanzhou362000 China

The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations. © Published under licence by IOP Publishing Ltd.

关键词： Electric power utilization

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Lightweight spatial pyramid pooling network for real-time semantic segmentation

Lightweight spatial pyramid pooling network for real-time se...

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2021 International Symposium on Intelligent Robotics and Systems, ISoIRS 2021

作者： Jiang, Peilong Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China

In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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A Dynamic Community Detection Algorithm Based on Parallel Incremental Related Vertices 2

A Dynamic Community Detection Algorithm Based on Parallel In...

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2017 IEEE 2nd International Conference on Big data Analysis（ICBDA 2017）

作者： Guohui Li Kun Guo YuZhong Chen Ling Wu Danhong Zhu College of Mathematics and Computer Sciences Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing(Fuzhou University) Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education

ISBN: (纸本)9781509036202

One of the traditional ways for detecting dynamic communities is to find the communities at each interval through the static community detection ***,it usually leads to high computation *** this paper,a novel algorithm based on the Map Reduce model and the label propagation progress with the strategy of incremental related vertices is proposed,which is called PLPIRV(Parallel Label Propagation and Incremental Related Vertices).Based on the communities found at the previous interval,the new algorithm adjusts the communities the incremental related vertices belong *** clustering of the whole network can be avoided by incrementally analyzing the variation of the networks,so that the time cost can be greatly *** on artificial and real datasets show that the proposed algorithm performs well on dynamic community detection.

关键词： dynamic community detection parallel computation incremental algorithm label propagation

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Deep Learning-based Multi-task Network for Intelligent Management of Garbage Deposit Points 11

Deep Learning-based Multi-task Network for Intelligent Manag...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Wang, Yezhen Zheng, Haobin Mao, Changjiang Zhang, Jing Ke, Xiao Fuzhou University College of Computer and Data Science China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9781665406796

With the economic and social development and the substantial improvement of material conditions, the generation of domestic waste has grown rapidly and has become a constraint factor for the development of new urbanization. In the past few years, research on the domestic waste industry has been limited to intelligent waste sorting, neglecting the role of intelligent management of waste storage sites. To relieve it, We propose a deep learning-based multi-task network for intelligent management of garbage deposit points, which combines algorithms such as YoloV5,Deepsort, Insightface, and Openpose to achieve waste bin detection, waste bin status recognition and analysis, face recognition, action recognition, and multiple object tracking based on real-time surveillance video. Besides, we propose a new dataset named Waste Bin Status, which provides a meaningful addition to the existing field of waste bin identification. Experiments on WBS dataset validate that our method is superior to other methods for garbage point status identification. Moreover, our network is trained to work with different scenarios of garbage deposits, demonstrating state-of-the-art performance in real-world tests. © 2021 IEEE.

关键词： Deposits

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A High-Order Scheme for Fractional Ordinary Differential Equations with the Caputo-Fabrizio Derivative

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Communications on Applied Mathematics and Computation 2020年第2期2卷 179-199页

作者： Junying Cao Ziqiang Wang Chuanju Xu School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen UniversityXiamen 361005China

In this paper, we consider numerical solutions of fractional ordinary diferential equations with the Caputo-Fabrizio derivative, and construct and analyze a high-order time-stepping scheme for this equation. The proposed method makes use of quadratic interpolation function in sub-intervals, which allows to produce fourth-order convergence. A rigorous stability and convergence analysis of the proposed scheme is given. A series of numerical examples are presented to validate the theoretical claims. Traditionally a scheme having fourth-order convergence could only be obtained by using block-by-block technique. The advantage of our scheme is that the solution can be obtained step by step, which is cheaper than a block-by-block-based approach.

关键词： Caputo-Fabrizio derivative Fractional diferential equations High-order numerical scheme

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Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery

Parallel Multi-Label Propagation Based on Influence Model an...

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作者： Qiu, Qirong Guo, Wenzhong Chen, Yuzhong Guo, Kun Li, Rongrong School of Economics and Management Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350116 China

Finding communities in networks is one of the challenging issues in complex network research. We have to deal with very large networks that contain billions of vertices, which makes community discovery a computationally intensive work. Moreover, communities usually overlap each other, which greatly increases the difficulty of identifying the boundaries of communities. In this paper, we propose a parallel multi-label propagation algorithm (PMLPA) that enhances traditional multi-label propagation algorithm (MLPA) in two ways. First, the critical steps of MLPA are parallelized based on the MapReduce model to get higher scalability. Second, new label updating strategy is used to automatically determine the most valuable labels of each vertex. Furthermore, we study the improvement of PMLPA through considering the influence of vertices and labels on label updating. In this way, the importance of each label can be described with higher precision. Experiments on artificial and real networks prove that the proposed algorithms can achieve both high discovering accuracy and high scalability. © 2017 World Scientific Publishing Company.

关键词： Complex networks

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