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International Conference on data engineering

作者： Ruijie Miao Fenghao Dong Yikai Zhao Yiming Zhao Yuhan Wu Kaicheng Yang Tong Yang Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github.

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Explore the collision safety of tram and passenger vehicle under disparate right of way 6

Explore the collision safety of tram and passenger vehicle u...

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6th International Conference on Traffic engineering and Transportation System, ICTETS 2022

作者： Guo, Yueling Liu, Yugang Xiao, Hanlin Ye, Jinsong School of Transportation and Logistics Southwest Jiaotong University Sichuan Province Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Province Chengdu610031 China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Sichuan Province Chengdu610031 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport China Academy of Transportation Sciences Beijing100029 China

ISBN: (纸本)9781510663053

Unlike traditional railway vehicles, the particularity of tram right of way determines the multiple and diversity of its accident forms. Based on the analysis of accident data at home and abroad, this paper determines the three road right management modes with the highest accident rate. On this basis, three collision scenarios are designed between a three-module 100% low-floor tram and various types of passenger vehicles. In this paper, we innovatively use PC-Crash crash simulation combined with SPSS quantitative analysis to study the accident characteristics of tram and passenger vehicle collisions under disparate right of way. The results show that the occupant's living space is smaller in 45° side impact under the semi-independent right of way. Under the mixed right of way, when the tram is used as a rear-end vehicle, the collision interface force is more intense. At the same collision speed, the displacement of the vehicle body and the invasion of the living space will be larger under the mixed right of way. Finally, based on the simulation results and causal analysis method, an accurate and highly flexible accident cause analysis framework is formed. From the aspects of single influence and comprehensive influence, the factors affecting the safety of tram operation under diverse road right management modes are analyzed, and tailored suggestions are given to improve the safety of the track system. © 2023 SPIE.

关键词： Highway planning

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Imaging Detection and Classification for Near-space Dim Targets 7

Imaging Detection and Classification for Near-space Dim Targ...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of engineering & technology 2023.

关键词： Extraction

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Leveraging Interactive Paths for Sequential Recommendation 28th

Leveraging Interactive Paths for Sequential Recommendation

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28th International Conference on database Systems for Advanced applications, DASFAA 2023

作者： Li, Aoran Zang, Yalei Wang, Yani Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China

ISBN: (纸本)9783031306716

Sequential recommendation systems dynamically predict the users’ next behaviors from chronological historical records to provide more accurate recommendations. However, in many real-world scenarios, recommenders find it difficult to make recommendations for cold-start users who have limited interactions with items. To address this issue, existing methods utilize auxiliary information to capture user preferences, which have achieved performance improvement to some extent. But with the gradual awareness of personal privacy, external information becomes difficult to obtain. Therefore, we develop a novel model called Path-based sequential Recommender for cold-start users (PathRec). PathRec exploits the rich sequential dependencies within the interactive paths to give reasonable recommendations for cold-start users without additional external information. Specifically, (i) PathRec constructs the global graph to learn the representations of the items. (ii) Then, PathRec elaborately selects the sampled paths to derive the underlying dependencies of the cold-start sequence. (iii) Finally, PathRec provides rich and complementary information to user-item interactions by strongly coupling the paths to give suitable recommendations. Extensive experiments on three datasets demonstrate the effectiveness of PathRec. In particular, our model can adapt to the cold-start users and has potential interpretability for recommendations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

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International Journal of Agricultural and Biological engineering 2024年第4期17卷 265-275页

作者： Weixin Zhai Zhi Xu Jinming Liu Xiya Xiong Jiawen Pan Sun-Ok Chung Dionysis Bochtis Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Department of Smart Agricultural Systems Graduate SchoolChungnam National UniversityDaejeon 34134Republic of Korea Institute for Bio-Economy and Agri-Technology(iBO) Centre for Research and Technology-Hellas(CERTH)Thessaloniki 57001Greece

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory *** the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was ***,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory ***,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature ***,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory *** validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural *** the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.

关键词： road-field trajectory classification efficientNet feature deformation network multi-range feature enhancement agricultural machinery operation mode recognition

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Combining spectral and textures of digital imagery for wheat aboveground biomass estimation

Combining spectral and textures of digital imagery for wheat...

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2022 International Conference on Electronic Information technology, EIT 2022

作者： Zheng, Ling Tao, Jianpeng Bao, Qian Weng, Shizhuang Zhang, Yakun Zhao, Jinling Huang, Linsheng National Engineering Research Venter for Agro-Ecological Big Data Analysis and Application Anhui University Hefei230601 China College of Agricultural Equipment Engineering Henan University of Science and Technology Luoyang471003 China

ISBN: (纸本)9781510655096

The aboveground biomass (AGB) is a key index for predicting wheat yield. In the case of high biomass, the AGB estimation of single spectral feature or image texture is poor. Therefore, this study evaluated the ability of fusion of spectral reflectance and texture to predict wheat AGB. Among them the reflectance spectrum of the wheat canopy was collected by near-earth spectrometer, and the texture features of three bands of RGB were extracted by gray co-occurrence matrix. Partial least squares regression (PLS) model was used to evaluate the relationship between fusion features and AGB. The experimental results based on the validated data set show that the AGB estimation effect of feature fusion is better than that of single feature (R2 = 0.70;RMSE = 0.06). This shows that the combination of spectral reflectance and texture can improve the accuracy of AGB estimation in the later stage. © 2022 SPIE. All rights reserved.

关键词： Textures

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Research on Equipment Operation and Maintenance Management technology of Large Railway Passenger Station 5th

Research on Equipment Operation and Maintenance Management T...

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5th International Conference on Smart Vehicular technology, Transportation, Communication and applications, VTCA 2022

作者： Wang, Bozhou Li, Lexi Ni, Shaoquan Chen, Dingjun School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Sichuan Chengdu610031 China

ISBN: (纸本)9789819908479

With the development of informationization and intelligence of railway passenger stations, problems such as inconvenient information interaction, missing operation and maintenance data, and difficulty in accurate positioning of equipment under the existing equipment operation and maintenance management mode have become the focus. In this paper, the operation and maintenance management process of equipment is divided into three stages: fault prediction and warning, fault diagnosis and processing, and fault rule summary. The implementation schemes of key technologies such as data warehouse, data mining, 5G fusion positioning, and electronic fence are given to realize functions such as condition assessment, fault prediction, fault diagnosis, precise positioning, fence warning, and auxiliary decision-making, which can meet the needs of managers and operations people. Research will help to improve the efficiency and safety of equipment operation and maintenance, and have a reference significance for integrated intelligent operation and maintenance technology and the construction of modern passenger stations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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SHE: A Generic Framework for data Stream Mining over Sliding Windows 22

SHE: A Generic Framework for Data Stream Mining over Sliding...

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Proceedings of the 51st International Conference on Parallel Processing

作者： Yuhan Wu Zhuochen Fan Qilong Shi Yixin Zhang Tong Yang Cheng Chen Zheng Zhong Junnan Li Ariel Shtul Yaofeng Tu School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China and PCL Research Center of Networks and Communications Peng Cheng Laboratory China School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China National University of Defense Technology China Redis Labs Israel ZTE Corporation China

ISBN: (纸本)9781450397339

1data stream mining over a sliding window is a fundamental problem in many applications, such as financial data trackers, intrusion detection and QoS. To meet the demand for high throughput of high speed data streams, sliding window algorithms turn to hardware platforms including FPGA/ASIC and programmable switches. These hardware platforms have three constraints for algorithms running on, which are 1) small memory usage 2) single stage memory access and 3) limited concurrent memory access. Algorithms perfectly fit in with these constraints will enable a highest utilization of these hardware platforms. However, no existing sliding window algorithm is specifically designed for hardware platforms. In this paper, we propose the Sliding Hardware Estimator (SHE), which is a generic framework that extends existing fixed window algorithms to sliding windows on hardware platforms. The key idea of SHE is that, during insertions we approximately delete out-dated information with little time and space overhead, while during queries we design sophisticated techniques to minimize error. We have fully implemented our SHE on FPGA, achieving a throughput of 544 Mips. We apply SHE to four typical data stream mining tasks. Experimental results show that, when compared with the state-of-the-art which cannot be implemented in hardware, SHE reduces the error by up to 100 times in membership queries. All related source codes are released at Github.

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Inverse Markov Process Based Constrained Dynamic Graph Layout

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Journal of Computer Science & technology 2021年第3期36卷 707-718页

作者： Shi-Ying Sheng Sheng-Tao Chen Xiao-Ju Dong Chun-Yuan Wu Xiao-Ru Yuan ParisTech Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Machine Perception(Ministry of Education) National Engineering Laboratory for Big Data Analysis and ApplicationPeking UniversityBeijing 100080China

In online dynamic graph drawing,constraints over nodes and node pairs help preserve a coherent mental map in a sequence of *** the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph *** existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting *** solve this problem,we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change,according to which different constraints can be *** constraints,along with stress function,generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent *** demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.

关键词： graph drawing data stream dynamic graph layout

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SC-GNN: A Communication-Efficient Semantic Compression for Distributed Training of GNNs 24

SC-GNN: A Communication-Efficient Semantic Compression for D...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Jihe Wu, Ying Wang, Danghui School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798400706011

Training big graph neural networks (GNNs) in distributed systems is quite time-consuming mainly because of the ubiquitous aggregate operations that involve a large amount of cross-partition communication for collecting embeddings/gradients during the forward and backward propagation. To reduce the volume of the communication, some recent approaches focused on decaying each of connections via sampling, quantifying, or delaying until satisfactory trade-off are obtained between volume and accuracy. However, when applied to popular GNNs, those approaches are found to be bounded by a common volume/accuracy Pareto frontier which shows that the decaying for individual connection cannot further accelerate the aggregate of training. In this work, SC-GNN, a semantic compression of the cross-partition communication, is proposed to concentrate a group of connections as a high-level semantics and transmit to a target partition. Since carrying the overall intent of a group, the semantics can keep transferring the interactions, i.e., embeddings/gradients, between a pair of remote partitions until GNN models converge. In addition, a connection-pattern based differential optimization is proposed to further prune those weak connections, while guaranteeing the training accuracy. The results show that, for multi-field datasets, the compression rate of SC-GNN is 40.8 × higher than SOTA methods and the epoch time is reduced to 31.8% on average. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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