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High-Speed Railway 2024年第2期2卷 101-109页

作者： Jiaxu Guo Ding Ding Peihan Yang Qi Zou Yaping Huang School of Computer Science and Technology Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Big Data&Artificial Intelligence in Transportation Ministry of EducationBeijing 100044China

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining ***,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale *** the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault *** algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent ***,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the *** experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

关键词： High utility Quantitative Frequent pattern mining Related degree pruning Fixed pattern length

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Few-Shot Partial Multi-View Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11824-11841页

作者： Zhou, Yuan Guo, Yanrong Hao, Shijie Hong, Richang Luo, Jiebo Ministry of Education Hefei University of Technology The Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China Hefei University of Technology The School of Computer Science and Information Engineering Hefei230009 China University of Rochester The Department of Computer Science RochesterNY14627 United States

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views;(ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://***/zhouyuan888888/UGDA. © 1979-2012 IEEE.

关键词： Neural networks

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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Exercise-induced adaptation of neurons in the vertebrate locomotor system

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Journal of Sport and Health science 2024年第2期13卷 160-171页

作者： Yue Dai Yi Cheng Renkai Ge Ke Chen Liming Yang Key Lab of Adolescent Health Assessment and Exercise Intervention of Ministry of Education College of Physical Education and Health CareEast China Normal UniversityShanghai 200241China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic EngineeringEast China Normal UniversityShanghai 200241China School of Physical Education and Health Care East China Jiaotong UniversityNanchang 330013China Key Laboratory of High Confidence Software Technologies of Ministry of Education School of Computer SciencePeking UniversityBeijing 100871China

Vertebrate neurons are highly dynamic cells that undergo several alterations in their functioning and physiologies in adaptation to various external *** particular,how these neurons respond to physical exercise has long been an area of active *** of the vertebrate locomotor system’s adaptability suggest multiple mechanisms are involved in the regulation of neuronal activity and properties during *** this brief review,we highlight recent results and insights from the field with a focus on the following mechanisms:(a)alterations in neuronal excitability during acute exercise;(b)alterations in neuronal excitability after chronic exercise;(c)exercise-induced changes in neuronal membrane properties via modulation of ion channel activity;(d)exercise-enhanced dendritic plasticity;and(e)exercise-induced alterations in neuronal gene expression and protein *** hope is to update the community with a cellular and molecular understanding of the recent mechanisms underlying the adaptability of the vertebrate locomotor system in response to both acute and chronic physical exercise.

关键词： Dendritic plasticity Excitability Exercise Ion channel modulation Neuron adaptation

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A potential game-based outsourcing computation framework for multiple calculators

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Digital Communications and Networks 2022年第5期8卷 654-663页

作者： Duo Zhang Youliang Tian Linjie Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Mathematics and Statistics Guizhou UniversityGuiyang550025China

Outsourcing computation enables a computationally weak client to outsource the computation of a function f to a more powerful but untrusted *** traditional outsourcing computation model forbids communication between players,but it has little *** on the game theory,this paper establishes an outsourcing computation model which is more in line with the actual ***,we construct a structural mapping relationship between security outsourcing computation and the optimization ***,by designing the individual potential function and the global potential function,the individual goal is consistent with the global goal to ensure the correctness of the calculation ***,in the information exchange environment between calculators,we construct a Zero-determinant strategy to ensure that the calculator chooses the strategy according to the predetermined target.

关键词： Outsourcing computation Game theory Potential function Zero-determinant strategy Information exchange

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Planning with Logical Graph-Based Language Model for Instruction Generation 27

Planning with Logical Graph-Based Language Model for Instruc...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhang, Fan Jin, Kebing Zhuo, Hankz Hankui School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9781643685489

Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture strict logic from free-form texts. In this paper, we propose a novel graph-based language model, Logical-GLM, to extract strict logic from free-form texts and then infuse into language models. Specifically, we first capture information from natural language instructions and construct logical probability graphs that generally describe domains. Next, we generate logical skeletons to guide language model training, infusing domain knowledge into language models. At last, we alternately optimize the searching policy of graphs and language models until convergence. The experimental results show that Logical-GLM is both effective and efficient compared with traditional language models, despite using smaller-scale training data and fewer parameters. Our approach can generate instructional texts with more correct logic owing to the internalized domain knowledge. Moreover, the search of logical graphs reflects the inner mechanism of the language models, which improves the interpretability of black-box models. © 2024 The Authors.

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A New Model for Network Security Situation Assessment of the Industrial Internet

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computers, Materials & Continua 2023年第5期75卷 2527-2555页

作者： Ming Cheng Shiming Li Yuhe Wang Guohui Zhou Peng Han Yan Zhao College of Computer Science and Information Engineering Harbin Normal UniversityHarbin150025China School of Information Technology Luoyang Normal UniversityLuoyang471934China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai200240China

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment ***,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative ***,the evaluation indicators are fused with expert knowledge and the ER *** to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model *** method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial ***,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient ***,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the *** research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet *** can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

关键词： Industrial internet network security situation assessment evidential reasoning belief rule base projection covariance matrix adaptive evolution strategy

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Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

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Projected heat wave increasing trends over China based on combined dynamical and multiple statistical downscaling methods

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Advances in Climate Change Research 2023年第5期14卷 758-767页

作者： Ming ZHANG Zhong-Yang GUO Guang-Tao DONG Jian-Guo TAN Key Laboratory of Geographic Information Science(Ministry of Education) East China Normal UniversityShanghai 200241China School of Geographic Sciences East China Normal UniversityShanghai 200241China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources of ChinaShanghai 200241China Shanghai Climate Center Shanghai 200030China Key Laboratory of Cities'Mitigation and Adaptation to Climate Change in Shanghai China Meteorological AdministrationShanghai 200030China Shanghai Meteorological IT Support Center Shanghai 200030China China Meteorological Administration Urban Meteorology Key Laboratory Beijing 100089China

Extensive investigations on the projection of heat waves(HWs)were conducted on the basis of coarse-resolution global climate models(GCMs).However,these investigations still fail to characterise the future changes in HWs regionally over *** dynamical downscaling with a horizontal resolution of 25 km×25 km was employed on the basis of GCM-HadCM3 to provide reliable projections on HWs over the Chinese mainland,and six statistical downscaling methods were used for bias correction under RCP4.5 and RCP8.5 *** multi-method ensemble(MME)of the top three dynamical downscaling methods with good performance was used to project future *** showed that PRECIS primarily replicated the detailed spatiotemporal pattern of ***,PRECIS overestimated the HWs in the Northwest and Southeast and expanded the areas of HWs in the Northeast and *** statistical downscaling methods(quantile mapping,CDF-t and quantile delta mapping)demonstrated good performance in improving PRECIS simulation for reproducing *** contrast,parametric-based trend-preserving approaches such as scaled distribution mapping and ISI-MIP are outperformed by the three aforementioned methods in downscaling HWs,particularly in the high latitudes of *** on MME projections,at the end of the 21st century,the national average of the number of HW days each year,the length of the longest HW event in the year and the extreme maximum temperature in HW will increase by 3 times,1 time and 1.3℃,respectively,under the RCP4.5 scenario,whilst that under the RCP8.5 scenario will increase by 8 times,3 times and 3.7℃,respectively,relative to *** Northwest is regionally projected to suffer long and hot HWs,whilst the South and Southeast will experience frequent consecutive ***,HWs projected by the combined dynamical and statistical downscaling method are highly reliable in projecting HWs over China.

关键词： Dynamical downscaling Statistical downscaling Heat waves Climate change

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Yangdong Chen, Mohan Zhang, Yuejie Feng, Rui Zhang, Tao Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (纸本)9798350368741

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research. © 2025 IEEE.

关键词： Diffusion Models Point Cloud Generation Self-supervised Contrastive Learning Sketch

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