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Journal of Network Intelligence 2025年第1期10卷 339-352页

作者： Cui, Xiu-Yan Hwang, David Hebei Software Institute Hebei Province University Intelligent Interconnection Equipment and Multimodal Big Data Application Technology Research and Development Center Baoding071000 China College of Engineering St. Paul University Philippines Tuguegarao3500 Philippines

Aiming at the issue that the jobs predicted by the existing job recommendation methods cannot be precisely matched with the job seekers, this paper designs a personalized intelligent job recommendation method based on deep learning in the Elas-ticsearch environment. Firstly, to deal with the data sparsity and cold start issue of traditional collaborative filtering algorithms, the similarity calculation formula of Pearson’s correlation in the algorithm is modified to enhance the accuracy of similarity calculation. Secondly, on the ground of the history of different job seekers, the keywords of job seekers are constructed in terms of various weights, and input into ElasticSearch to recall the set of jobs that are strongly related to the job seekers. Then adopting Convolutional Neural Network (CNN) to extract the text data features of job seekers and positions, and meanwhile, integrating the attention mechanism to get the auxiliary features of job seekers and positions, and finally, integrating the implicit feedback data with other at-tribute features of job seekers and positions as the input of the improved collaborative filtration algorithm, so that we can realize the intelligent recommendation of jobs for job seekers. Simulation outcome indicates that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Root Mean Square Percentage Error (RMSPE) of the suggested method are 0.0681, 0.0519, 0.0834 and 0.0628 respectively, which are lower than those of the comparison method, and the accuracy of the recommendation is effectively enhanced. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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An Application Level Synchronization Method for Heterogeneous Redundant Structures 21

An Application Level Synchronization Method for Heterogeneou...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yu, Hong Liu, Qinrang Cui, Chao Wei, Shuai Information Engineering University Information & Technology Research Center Zhengzhou China Phytium Technology Co. Ltd. Software Research & Development Department Tianjin China

ISBN: (纸本)9798350329223

In heterogeneous redundant architecture devices, programs with equivalent functions are implemented on heterogeneous executers, and the scheduler needs to compare their outputs to identify abnormality and decide the final output. Due to slight differences in the scheduling order of threads among different executers, the outputs of programs are likely to be different, which will have a significant impact on the arbitration of the scheduler, causing false error report and affecting the normal processing. We propose an application-level state synchronization method model to effectively mitigate the problem of program state synchronization of multiple heterogeneous executers. And we implemented a hardware-software codesign of the method in a DHRA switch and tested its effectiveness and latency. Tests show that the method is effective with latency within several milliseconds. © 2023 IEEE.

关键词： Application programs

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KRLGI: Knowledge Representation Learning Based on Global Information for Reasoning 36

KRLGI: Knowledge Representation Learning Based on Global Inf...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Wu, Bo Mao, Yingchi Chi, Fudong Liu, Yingjie Ding, Silong Qi, Rongzhi Hohai University College of Computer Science and Software Engineering Nanjing China Huaneng Lancang River Hydropower Inc Technology Research and Development Center Kunming China

ISBN: (纸本)9798331527235

Knowledge Graph Reasoning based on Representation Learning maps the entities and relations into a vector space, assessing entity-pair similarities to deduce unknown facts. However, existing methods focus solely on the local importance of entities and ignore isolated entities, leading to the issue of missing feature information. To solve this, we propose Knowledge Representation Learning based on Global Information (KRLGI). KRLGI adopts an attention-based biased random walk algorithm to obtain global information and determine the importance of global entities. The global entity importance goes through a conversion into attention weights, and these weights are integrated with the local entity importance. Subsequently, the local and global entity importance can be used together to represent entity embeddings. Globally integrated representations can reveal richer semantics and enhance reasoning capabilities. Experimental results indicate that KRLGI outperforms other baselines in reasoning accuracy on four public datasets. Notably, on the FB15k-237 dataset, KRLGI shows significant improvements over the best baseline, with increases of 6.37% in MRR, 2.5% in HITS@1, and 5.5% in HITS@10. © 2024 IEEE.

关键词： Knowledge graph

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LRIRL: Improving Knowledge Graph Reasoning through Representation Learning-Based Rule Induction 36

LRIRL: Improving Knowledge Graph Reasoning through Represent...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Liu, Yingjie Mao, Yingchi Chit, Fudong Wu, Bo Ding, Silong Qi, Rongzhi College of Computer Science and Software Engineering Hohai University Nanjing China Huaneng Lancang River Hydropower Inc Technology Research and Development Center Kunming China

ISBN: (纸本)9798331527235

Rule induction is an important approach for reasoning over Knowledge Graphs. Existing works mainly rely on searching for rule instances within the Knowledge Graph to induce rules. However, this approach may generate a vast search space, leading to inefficiency and difficulty in discovering rules that lack instance support. We propose a Logical Rule Induction based on Representation Learning (LRIRL) method, which can mine rules at the pattern level. By computing rule scores through vector representations of the rules, LRIRL can avoid the inefficiency caused by directly searching for rule instances in a vast search space. Furthermore, by incorporating the deductive nature of logical rules into the rule induction process, LRIRL can mine and evaluate rules even in the absence of rule instances. The generated rules can be used to perform more efficient and accurate reasoning tasks on the Knowledge Graph. Experimental results demonstrate that LRIRL outperforms baselines in both reasoning accuracy and rule mining efficiency on public datasets. Compared to the best baseline, LRIRL can achieve an average accuracy improvement of 3.43% in MRR, 1.40% in HITS@1, and 1.80% in HITS@10. Moreover, LRIRL can reduce rule mining time by an average of approximately 35% compared to the best baseline. © 2024 IEEE.

关键词： Knowledge graph

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High accuracy localization and docking for mobile robots in low dynamic environments 4

High accuracy localization and docking for mobile robots in ...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Hu, Lizhi Lu, Wei Li, Xiang Lin, Hui Zhejiang HuaRay Technology Co. Lt. Navigation and Localization Algorithm Department Hangzhou China Zhejiang HuaRay Technology Co. Lt. Software Research and Development Center Hangzhou China

ISBN: (纸本)9798350394375

Localization and Navigation technology of mobile robots is becoming increasingly important in production and manufacturing factory. It is necessary to ensure localization and docking accuracy at high accuracy points and routes. In this paper, we propose a high-accuracy localization method based on 2D laser, wheel odometry, and prior probability grid map. The algorithm can maintain the accuracy of robot trajectory estimation in an dynamic environment that changes over time. Two resolution prior probability grid maps are constructed to determine the initial pose of the mobile robot. A factor graph model is constructed, allowing multiple relative or absolute observations, to be added to the factor graph as factors. During localization, two local submaps are constructed in real time, and are jointly optimized with prior maps before being used to the local scan matching. The laser odometry is generated by using local submaps scan matching, and is added to the factor graph model along with the wheel odometry and prior map scan matching. At the same time, several artificial features are designed at high-accuracy docking target points, and ensure the final docking accuracy by identifying artificial features. © 2024 IEEE.

关键词： Mobile robots

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Fast flow field prediction of pollutant leakage diffusion based on deep learning

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Environmental Science and Pollution research 2024年第36期31卷 49393-49412页

作者： YunBo, Wan Zhong, Zhao Jie, Liu KuiJun, Zuo Yong, Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China School of Aeronautics Northwestern Polytechnical University Xi’an710072 China

Abstract: Predicting pollutant leakage and diffusion processes is crucial for ensuring people’s safety. While the deep learning method offers high simulation efficiency and superior generalization, there is currently a lack of research on predicting pollutant leakage and diffusion flow field using deep learning. Therefore, it is necessary to conduct further studies in this area. This paper introduces a two-level network method to model the flow characteristics of pollutant diffusion. The proposed method in this study demonstrates a significant enhancement in flow field prediction accuracy compared to traditional deep learning methods. Moreover, it improves computational efficiency by over 800 times compared to traditional computational fluid dynamics (CFD) methods. Unlike conventional CFD methods that require grid expansion to calculate all operation conditions, the deep learning method is not confined by grid limitations. While deep learning methods may not entirely replace CFD methods, they can serve as a valuable supplementary tool, expanding the versatility of CFD methods. The findings of this research establish a robust foundation for incorporating deep learning methods in addressing pollutant leakage and diffusion challenges. Graphical abstract: A deep learning method for simulating pollutant leakage diffusion. (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Diffusion

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Exploring the Optimization of Urban Ecological Spaces from a Perspective of Ecosystem Service Functions based on Remote sensing Imagery

Exploring the Optimization of Urban Ecological Spaces from a...

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2022 International Conference on Geographic Information and Remote Sensing technology, GIRST 2022

作者： Zhao, Ziyan Fang, Hui Zhou, Haiwei Liu, Tingzhou Zhang, Wei School of Software Technology Zhejiang University Hangzhou China Ningbo Rail Transit Ningbo China Hangzhou Real Estate Development Research Center Hangzhou China

ISBN: (纸本)9781510662186

As ecological priority has become a central theme of current development, the Yangtze River Delta Eco-Green Integrated development Demonstration Zone is contributing as a guiding model to China's ecological and green integrated development. From a perspective of ecosystem service functions, in particular, geographical locations and land habitats for animals and plants, we provide an analysis of the aforementioned Demonstration Zone regarding the distribution, connectivity, and accessibility of its ecological sources, in order to select strategic zones and optimize ecological spaces, from remote sensing imagery. Our analysis was based on the evolution of the ecological base and the Sources-Resistance Surface-Corridors framework integrating local conditions and characteristics. Through navigating remote sensing images of the target area, results indicated that a considerable change occurred in the plant community in the Demonstration Zone over the recent three decades, caused by the severely damaged wetland ecosystem and the limited communications among species due to urban expansion. In addition, the study revealed poor connectivity among the ecological and geographical sources. Accordingly, we propose a set of measures to avoid landscape fragmentation, prioritize ecological protection, and improve the quality of habitats in the geographic location. © 2023 SPIE.

关键词： Demonstrations

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Propagation and Dispersion Characteristics of Scholte Wave in VTI Media with the Presence of Irregular Seabed

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Journal of Earth Science 2024年第2期35卷 722-725页

作者： Xiaobo Liu Yun Wang Jianlei Zhang Haifeng Chen Hua Zhang School of Ocean Sciences China University of GeosciencesBeijing 100083China Key Laboratory of Polar Geology and Marine Mineral Resources China University of GeosciencesBeijing 100083China School of Geophysics and Information Technology China University of GeosciencesBeijing 100083China Research&Development Center BGP IncCNPCZhuozhou 072751China National Engineering Research Center for Oil and Gas Exploration Computer Software Beijing 100080China

INTRODUCTION Surface waves propagating along the seafloor are general ly called Scholte waves,and were first discovered and studied in the early 1950s(Kugler et al.,2005;Buchen and Ben-Hador,1996).Scholte waves exhibit a dispersion phenomenon,which implies that their velocity varies with ***-frequency Scholte waves can propagate over long distances on the seabed with little attenuation(Bohlen et al.,2004).The particle mo tion of Scholte waves in a solid medium changes from a retro grade to a prograde ellipse(Klein et al.,2005;McMechan and Yedlin,1981).

关键词： seabed Dispersion implies

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AgileShard: Turning the Sharded Blockchain into a Real-Time Transaction Processing System 44

AgileShard: Turning the Sharded Blockchain into a Real-Time ...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Shi, Jianfeng Wu, Heng Liu, Wang Gao, Heran Zhang, Wenbo University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Software Engineering Technology Research and Development Center Institute of Software Chinese Academy of Sciences Beijing China Nanjing Institute of Software Technology Nanjing China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing China

ISBN: (纸本)9798350328578

Blockchain, as an emerging transaction processing system, suffers from low throughput and high latency. Sharded blockchains can significantly increase throughput by dividing nodes into groups (i.e., shards) to process disjoint transactions in parallel. However, the diverse latency requirements of transactions in current sharded blockchains are not well met. Three challenges prevent the sharded blockchain from becoming a real-time transaction processing system, namely, the static block size, the first-come-first-served transaction packing strategy, and the load imbalance. Therefore, this paper proposes 3 methods to help turn the sharded blockchain into a real-time transaction processing system. First, we propose an inter-shard dynamic block size negotiation method that enables shards to adaptively determine the globally optimal block size based on the deadlines of pending transactions. Then, we propose a DAG-based transaction packing method for reducing the number of deadline violations and improving parallelism. Finally, we propose a minimum-cost-flow-based shard reconfiguration method to address load imbalance. Under real datasets on Ethereum, experimental results show that AgileShard using the above three methods can effectively improve the transaction deadline satisfaction rate. © 2023 IEEE.

关键词： Blockchain

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MTT-Road: Dynamic Updating of Road Diseases Classification Model Based on Datasets Distillation 3

MTT-Road: Dynamic Updating of Road Diseases Classification M...

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3rd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Han, Haihang Ge, Yongjun Wang, Xinxiao Yu, Zhi Xi, Chenchen Wang, Yue Institute of Transport Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology Zhejiang Scientific Research Hangzhou China Zhejiang University School of Software Technology Hangzhou China Zhejiang University Polytechnic Institute Hangzhou China

ISBN: (纸本)9798350398410

The apparent diseases of road will cause great losses to driving safety and economic benefits, so when the apparent diseases of road appear, we need to detect them quickly and accurately. Engineers need to use the classified error data in the inspection process to continually and dynamically update the model to avoid similar errors in the subsequent inspection journey. The existing methods do not balance the resource consumption and model effect in the iterative update of the model. In order to solve the problem of continual dynamic update of road disease classification model, the MTT-Road algorithm is proposed in this *** experimental results show that the MTT-Road algorithm achieves the best results among the mainstream transfer learning algorithms considering both the time consumption and model effect. © 2023 IEEE.

关键词： Image classification

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