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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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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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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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development Strategy and Countermeasures of China's CBM Industry under the Goal of “Carbon Peak and Neutrality”

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Journal of Earth Science 2023年第4期34卷 975-984页

作者： Fengyin Xu Xia Yan Fenglin Wang Xinyuan Ma Jian Yun Hongna Wang Borui Xu Shuangyuan Zhang Delei Mao China United Coalbed Methane National Engineering Research Center Co. Ltd.Beijing 100095China Research Institute of Petroleum Exploration&Development PetroChina Coalbed Methane Co.Ltd.Beijing 100028China Research management department CNPC Research Institute of Safety and Environment Technology Co.Ltd.Beijing 102249China Exploration technical R&D Center CNPC Exploration Software Co.Ltd.Beijing 100080China

China’s coalbed methane(CBM) industry is in an extremely important “climbing period” and “strategic opportunity period”,which can be generally characterized by “three low and one small”:low degree of exploration and development,low adaptability of main technology,low return on investment and small development *** the “carbon peak and neutrality” background,the development status of CBM industry is systematically *** resources,technology,management problems and reasons are *** and countermeasures to accelerate the industrial development are put forward according to the factors such as efficient development of resources,major technical breakthrough,talent team training,policy formulation and implementation,return on investment and so *** existing problems are as follows:(a) The overall occurrence conditions in China are complicated and the development is difficult compared with the other countries.(b) The research precision accuracy of CBM resource conditions is not enough.(c) The adaptability of technology is poor.(d) The management mode is not *** view of these problems,this paper puts forward the “two steps” development strategy and the technical and management countermeasures of “five in one”.The corresponding “five in one” technology and management countermeasures are the formulation and implementation of relevant safeguard measures in accordance with the principle of collaborative innovation in five aspects:resources,technology,talents,policies and *** the above measures,the dream and grand blueprint of CBM industry shall be realized.

关键词： coalbed methane CBM industry carbon peak carbon neutrality development strate-gy countermeasures.

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Consistency Check for Multimodal Transportation Data: A Case Study of Vision and LiDARA 9

Consistency Check for Multimodal Transportation Data: A Case...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Liu, Zhengyu Chen, Zhenwu Yang, Xiangmin Peng, Lei Software Engineering Institute University of Science and Technology of China HeFei230026 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Research Center for Automotive Electronics ShenZhen518055 China Shenzhen Urban Transport Planning Center Co. Ltd. Innovation Center for ITS ShenZhen518063 China Shenzhen Shenzhentong Co. Ltd. Dept. Research and Development ShenZhen518131 China

ISBN: (纸本)9798350390025

This study proposes a novel consistency verification method for multimodal transportation data, targeting spatial feature issues caused by multisource heterogeneous data in the transportation field. We unify different sources of data and point cloud data into a single 3D space through image data dimensionality increase and point cloud registration, addressing data inconsistencies caused by perspective differences. Through the application of data dimensionality augmentation and point cloud registration, our method effectively resolves spatial feature discrepancies between various data sources, significantly improving the accuracy of consistency verification. Experimental results indicate that the model trained with our method exhibits superior precision in consistency verification of multisource heterogeneous data compared to traditional approaches. © 2024 IEEE.

关键词： Spatio-temporal data

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Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 307-315页

作者： Wang, Wei Chen, Jian Hu, Yuzhu Liu, Han Chen, Junxin Gadekallu, Thippa Reddy Garg, Lalit Guizani, Mohsen Hu, Xiping Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518000 China School of Software Dalian University of Technology Dalian116621 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India Faculty of Information and Communication Technology University of Malta Msida Malta United Arab Emirates

The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently. © 2024 The Authors

关键词： Internet of things

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning 37

CLGT: A Graph Transformer for Student Performance Prediction...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Identification of Electrical Equipment Based on V-I Trajectory, Odd Harmonic Currents 8

Identification of Electrical Equipment Based on V-I Trajecto...

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8th International Conference on Smart Grid and Smart Cities, ICSGSC 2024

作者： Fu, Yan Chen, Shengze Zhao, Fang Shi, Yvhan Ye, Siwen Jiang, Jianfeng Wang, Lei Yang, Xijun Electric Power Science Research Institute Shanghai Electric Power Company Shanghai China Software Development Center Shanghai China Shanghai Jiao Tong University Dept. of Electrical Engineering Shanghai China Beijing Zhixiang Technology Co. Ltd Beijing China

ISBN: (纸本)9798350377408

Non-intrusive load monitoring (NILM) and identification are important to smart grid construction and safe operation. A method combining mixed feature matrix and convolutional neural network based on V-I trajectory and odd harmonic components is proposed and proved for non-intrusive electrical load monitoring technology. In the paper, the specific contents are to analyze the basic characteristics of electrical equipment, including steady-state and transient characteristics of input current. Six simulation circuits of single-phase AC power supply powered electrical equipment are established by means of MATLAB/Simulink using Electrical or Electronic blockset, including heater, hair dryers LED string, adapter, variable frequency air conditioner (air-CON), and variable frequency refrigerators, as the electrical equipment to be analyzed and identified. The grid side current waveforms and spectrums, V-I trajectories, and odd harmonic current components of these six load models are extracted as basic features. The mixed feature matrix using V-I trajectory and odd harmonic features, as well as convolutional neural network (CNN), are proposed for the analysis and identification of electrical equipment. After training and testing on the PLAID dataset, the system can effectively identify electrical equipment under steady-state operation. In addition, three different detection methods are compared and analyzed to verify the superiority of the proposed method for analyzing and identifying electrical equipment based on mixed feature matrix and convolutional neural network. © 2024 IEEE.

关键词： Smart power grids

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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