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Proceedings of the 2024 8th International Conference on Computational Biology and Bioinformatics

作者： Gatis Melkus Andrejs Sizovs Sandra Silina Peteris Rucevskis Lelde Lace Edgars Celms Juris Viksna Research Laboratory of System Modeling and Software Technologies Institute of Mathematics and Computer Science University of Latvia Riga Latvia

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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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UAV-Mounted Intelligent Reflecting Surface in Maritime Wireless Powered Communication Network

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China Communications 2025年第3期22卷 254-269页

作者： Liu Yuan Han Fengxia Zhao Shengjie School of Information Science and Electrical Engineering Shandong Jiaotong UniversityJinan 250357China College of Electronic and Information Engineering Tongji UniversityShanghai 201804China School of Software Engineering Tongji UniversityShanghai 201804China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of EducationShanghai 200003China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804China

Intelligent reflecting surface(IRS)assisted with the wireless powered communication network(WPCN)can enhance the desired signal energy and carry out the power-sustaining problem in ocean monitoring *** this paper,we investigate a reliable communication structure where multiple buoys transmit data to a base station(BS)with the help of the unmanned aerial vehicle(UAV)-mounted IRS and harvest energy from the base station *** organically combine WPCN with maritime data collection scenario,a scheduling protocol that employs the time division multiple access(TDMA)is proposed to serve multiple buoys for uplink data ***,we compare the full-duplex(FD)and half-duplex(HD)mechanisms in the maritime data collection system to illustrate different performances under these two *** maximize the fair energy efficiency under the energy harvesting constraints,a joint optimization problem on user association,BS transmit power,UAV’s trajectory and IRS’s phase shift is *** solve the non-convex problem,the original problem is decoupled into several subproblems,and successive convex optimization and block coordinate descent(BCD)methods are employed obtain the near-optimal solutions *** results demonstrate that the UAV-mounted IRS can significantly improve energy efficiency in our considered system.

关键词： full-duplex IRS joint optimization TDMA UAV WPCN

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FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning 26

FedWNS: Data Distribution-Wise Node Selection in Federated L...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Tu, Chengwu Zhao, Shengjie Deng, Hao Tongji University School of Software Engineering Shanghai201800 China Key Laboratory of Embedded System and Service Computing Shanghai201800 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201800 China

ISBN: (纸本)9798350331684

To deal with the discrepancy between global and local objectives in the federated learning invoked by the non-independent, identically distributed (non-IID) data and mitigate the impact of catastrophic forgetting in the training phase, we propose a federated learning framework with data distribution-wise reinforcement learning to perform node selection to accelerate the convergence process and alleviate the accuracy degradation. In this framework, the agent on the central server observes the number of samples every node owns, the derived distribution information of every dataset, and the current local and global accuracy. Then infer the selected node-set to participate in the current federated learning round through policy network in reinforcement learning. Finally, we conduct simulations with publicly data sets. Simulation results indicate that our FedWNS outperforms the existing FedAvg and CSFedAvg on the testing accuracy and the communication rounds to reach target accuracy under different settings. © 2023 IEEE.

关键词： Reinforcement learning

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FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning

FedWNS: Data Distribution-Wise Node Selection in Federated L...

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International Conference on Computer Supported Cooperative Work in Design

作者： Chengwu Tu Shengjie Zhao Hao Deng School of Software Engineering Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

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Decision Tree Pruning Method Using Delayed Sampling 38

Decision Tree Pruning Method Using Delayed Sampling

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38th International Conference on Information technologies, InfoTech 2024

作者： Mitrofanov, Sergei Alexandrovich Semenkin, Eugene Stanislavovich Reshetnev Siberian State University of Science and Technology Institute of Informatics and Telecommunications Department of System Analysis and Operations Research Krasnoyarsky Rabochy Av. 31 Krasnoyarsk Russia Bauman Moscow State Technical University Scientific and Educational Center "Artificial Intelligence Technologies" 2nd Baumanskaya St. 5 Moscow Russia Siberian Federal University Laboratory "Hybrid Methods of Modeling and Optimization in Complex Systems" Academic Kirensky St. 26a Krasnoyarsk Russia

ISBN: (纸本)9798350365689

Algorithms based on decision trees have been around for quite some time, however, they are still one of the most popular because of their efficiency and the interpretability of their results. One of the main problems of decision trees is their high tendency to overfit. There are various ways to solve this problem, but this paper proposes a new method based on tree pruning using delayed sampling. The tree is trained using the Separation Measure and Differential Evolution algorithms. The proposed approach improves the standard method and is compared with it. The proposed approach demonstrates higher efficiency than the standard algorithm, and also improves the interpretability of the resulting trees. © 2024 IEEE.

关键词： Adversarial machine learning

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Super-resolution reconstruction of a 3 arc-second global DEM dataset

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Science Bulletin 2022年第24期67卷 2526-2530,M0003页

作者： Bo Zhang Wei Xiong Muyuan Ma Mingqing Wang Dong Wang Xing Huang Le Yu Qiang Zhang Hui Lu Danfeng Hong Fan Yu Zidong Wang Jie Wang Xuelong Li Peng Gong Xiaomeng Huang Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change StudiesDepartment of Earth System ScienceTsinghua UniversityBeijing 100084China Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Huawei Technologies Co.Ltd Hangzhou 310007China Peng Cheng Laboratory Shenzhen 518000China School of Artificial Intelligence OPtics and ElectroNics(iOPEN)Northwestern Polytechnical UniversityXi’an 710072China Department of Geography and Department of Earth Sciences University of Hong KongHong KongChina

Obtaining multi-source satellite geodesy and ocean observation data to map the high-resolution global digital elevation models(DEMs)is a formidable task at *** key theoretical and technological obstacles to fine modeling must be overcome before the geographical ditribution of ocean topography and plate move-ment patterns can be ***.

关键词：迁移学习超分辨率卫星大地测量预训练地形复杂度地球圈层均衡机制弧秒

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Decision Tree Pruning Method Using Delayed Sampling

Decision Tree Pruning Method Using Delayed Sampling

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International Conference on Information technologies (InfoTech)

作者： Sergei Alexandrovich Mitrofanov Eugene Stanislavovich Semenkin Department of System Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russian Federation Scientific and Educational Center "Artificial Intelligence Technologies" Bauman Moscow State Technical University Moscow Russian Federation Laboratory "Hybrid Methods of Modeling and Optimization in Complex Systems" Siberian Federal University Krasnoyarsk Russian Federation

ISBN: (数字)9798350365689

ISBN: (纸本)9798350365696

关键词： Visualization Machine learning algorithms Accuracy Measurement uncertainty Machine learning Sampling methods Decision trees Stakeholders Information technology Standards

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Att-TCN: An Advertising Attribution Method Based on Temporal Convolutional Network with Attention Mechanism

Att-TCN: An Advertising Attribution Method Based on Temporal...

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International Conference on Computer and Communications (ICCC)

作者： Bowen Xie Yi Sun Zhaowen Lin Baoquan Ma School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Technologies Beijing China National Computer System Engineering Research Institute of China Beijing China National Engineering Laboratory for Industry Control System Information Security Technology Beijing China

Advertising attribution is an effective method to measure the effect of various advertisements in the era of Internet advertising. It enables scientific tracking back and refined operation of advertising by quantitatively evaluating the contribution of different advertising channels to reach the target. It is challenging to predict conversion results based on users' journey data accurately, reflect the impact of ads on user conversions, and appropriately assign attribution values to advertising channels. To achieve the above objectives, we propose an advertising attribution method based on a temporal convolutional network with an attention mechanism (Att-TCN). By mining the hidden information in time and attribution channels using the attention mechanism, our proposed method can improve the accuracy of conversion prediction and thus more rationally assign attribution values across channels. Experimental validation on a real dataset obtained from an operational commercial advertising monitoring company shows that our proposed attribution method has better accuracy than other state-of-the-art attribution methods and gives an open-ended approach to attribution value assignment.

关键词： Target tracking Costs Companies Predictive models Internet Convolutional neural networks Data mining

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Arabic Sentiment Analysis Problems and Challenges

Arabic Sentiment Analysis Problems and Challenges

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Virtual Campus (JICV), X International Conference on

作者： Youssra Zahidi Yacine EL Younoussi Yassine AL-Amrani Information System and Software Engineering Laboratory Abdelmalek Essaadi University Tetuan Morocco Information Technology and Modeling Systems (TIMS) research tea Abdelmalek Essaadi University Tetuan Morocco

The following topics are dealt with: computer aided instruction; teaching; educational courses; educational institutions; further education; distance learning; Internet; computer science education; physics education; ... 详细信息

ISBN: (数字)9781665415194

ISBN: (纸本)9781665430203

关键词： Sentiment analysis Social networking (online) Education Tools

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