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Research on key technologies of edge cache in virtual data space across WAN

Frontiers of Computer Science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and Computer Network Information Center in Chinese Academy of Sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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Unsupervised Statistical Text Simplification

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IEEE Transactions on knowledge and data engineering 2021年第4期33卷 1802-1806页

作者： Qiang, Jipeng Wu, Xindong Department of Computer Science Yangzhou University Yangzhou Jiangsu225127 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui10084 China

Most recent approaches for Text Simplification (TS) have drawn on insights from machine translation to learn simplification rewrites from the monolingual parallel corpus of complex and simple sentences, yet their effectiveness strongly relies on large amounts of parallel sentences. However, there has been a serious problem haunting TS for decades, that is, the availability of parallel TS corpora is scarce or not fit for the learning task. In this paper, we will focus on one especially useful and challenging problem of unsupervised TS without a single parallel sentence. To the best of our knowledge, we present the first unsupervised text simplification system based on phrase-based machine translation system, which leverages a careful initialization of phrase tables and language models. On the widely used WikiLarge and WikiSmall benchmarks, our system respectively obtains 39.08 and 25.12 SARI points, even outperforms some supervised baselines. © 1989-2012 IEEE.

关键词： Machine translation

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Ordering Results on Largest Order Statistics from Multiple-Outlier Gamma Variables

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Communications in Mathematics and Statistics 2023年第2期11卷 257-282页

作者： Yiying Zhang Yanni Hu Peng Zhao School of Statistics and Data Science LPMC and KLMDASRNankai UniversityTianjin 300071People’s Republic of China SpeakIn Technologies Co. Ltd.Shanghai 200000People’s Republic of China School of Mathematics and Statistics and RIMS Jiangsu Provincial Key Laboratory of Educational Big Data Science and EngineeringJiangsu Normal UniversityXuzhou 221116People’s Republic of China

In this article,we carry out stochastic comparisons on the maximum order statistics arising from two batches of multiple-outlier gamma random variables with different shape and scale *** is proved that,under certain conditions,the majorization order between the vectors of shape parameters together with the weak majorization order[p-larger order]between the vectors of scale parameters implies the likelihood ratio order[hazard rate order]between the largest order *** results established here strengthen and generalize some known ones in the literature.

关键词： Gamma distribution Stochastic orders Largest order statistics Majorization p-Larger order

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Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Li, Zhenyu Wu, Ruixin Wang, Jin Hu, Chuanqi Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture. © 2025 IEEE.

关键词： Motion planning

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Glacial Lake Area Changes in High Mountain Asia during 1990-2020 Using Satellite Remote Sensing

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Research 2023年第2期2022卷 1-23页

作者： Meimei Zhang Fang Chen Huadong Guo Lu Yi Jiangyuan Zeng Bin Li International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesNo.9 Dengzhuang South RoadBeijing 100094China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Coastal Environment and Resources Research of Zhejiang Province School of EngineeringWestlake UniversityHangzhou 310024China State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of Sciences Beijing 100094China

Changes in a large-scale glacial lake area directly reflect the regional glacier status and climate ***,long time series of glacial lake dataset and comprehensive investigation of the spatiotemporal changes in the gla... 详细信息

关键词： glacial glacier lake

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knowledge-Guided Cross-Topic Visual Question Generation 30

Knowledge-Guided Cross-Topic Visual Question Generation

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Hongfei Wang, Guohua Xie, Jiayuan Chen, Jiali Fang, Wenhao Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9782493814104

Visual question generation (VQG) task aims to generate high-quality questions based on the input image. Current methods primarily focus on generating questions containing specified content utilizing answers or question types as constraints. However, these constraints make it challenging to control the topic of generated questions (e.g., conversation or test subject topics) for various applications. Thus, it is necessary to utilize topics as constraints to guide question generation. Considering that there are many topics and it is almost impossible for human annotations to cover them, we propose the cross-topic learning VQG (CTL-VQG) task, which aims to generate questions related to unseen topics in cross-topic scenarios. In this paper, we propose a knowledge-guided cross-topic visual question generation (KC-VQG) model to extract unseen topic-related information for question generation. Specifically, an image-topic feature extractor is introduced in our model to extract topic-related intuitive visual features;an image-topic knowledge extractor is used to extract and select the most appropriate topic-related implicit knowledge from large language models for generating questions. Extensive experiments show that our model outperforms baselines and can effectively generate unseen topic-related questions in cross-topic scenarios. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： data mining

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Fish Object Detection Based on Spatial Bias Pyramid Pooling: Improved BIAS-YOLO 8

Fish Object Detection Based on Spatial Bias Pyramid Pooling:...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhang, Chunhui Gu, Shenming Zhejiang Ocean University School of Information Engineering Zhoushan316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9798350302455

Object detection, as an important research direction of artificial intelligence technology, has a wide scope of application in several fields. At the same time, object detection technology shows a trend of multidisciplinary integration, which frees related researchers and some laborers from repetitive labor, thus improving the efficiency and accuracy of their work. Fishery resources play a pivotal role as an important agricultural resource, and the realization of different kinds of fish detection is of great help to the rational use and protection of fishery resources. However, there are some problems in the application of object detection technology to this field, such as the lack of datasets, and the quantity and quality of samples in the data set cannot satisfy the increasingly large neural network models. In this paper, we created a dataset dedicated to fish target detection through various channels such as photography and the web. The dataset contains 16 categories and 1038 images, including common fish (e.g., carp, hairtail, etc.) and also some precious fish such as grouper. We improved the quality of the images to enable the network to learn more accurate information about the characteristics of different fish. Meanwhile, we propose BIAS-YOLO, which improves the original SPP (Spatial Pyramid Pooling) module based on the original YOLOv5 network, and introduced the idea of spatial bias, which can encode the global information and thus improve the network to make full use of the whole image information. On our proposed dataset, our network works better compared to the baseline. © 2023 IEEE.

关键词： Object detection

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Secondary Access Class Barring for Machine Type Communications with PUSCH Resource Constraints 100

Secondary Access Class Barring for Machine Type Communicatio...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Wen, Yuxuan Yang, Liu Bai, Chaoyuan Liu, Heng Chengdu611756 China Southwest Jiaotong University Key Lab of Information Coding & Transmission Institude of Mobile Communication Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China

ISBN: (纸本)9798331517786

With the continuous increase of IoT devices, multiple devices desire to use network resources simultaneously during peak hours and overload conditions, leading to collisions and a reduction in the network's effective throughput. The Access Class Barring (ACB) method has been proposed as the optimal potential solution in LTE communication networks to overcome this problem. However, existing ACB schemes do not fully utilize preamble resources, and most schemes do not consider the situation where PUSCH resources are limited. Therefore, this paper proposes a two-stage ACB algorithm that considers the situation of limited PUSCH resources. This algorithm recycles part of the preamble resources and sets the ACB factor based on the number of PUSCH resources. Simulation results show that our proposed algorithm significantly outperforms traditional schemes in terms of total service time, delay, throughput, and PUSCH resource efficiency. © 2024 IEEE.

关键词： Internet of things

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Ship Target Detection Algorithm Based on Improved YOLOv5 3

Ship Target Detection Algorithm Based on Improved YOLOv5

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3rd International Conference on Computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Louyu Bi, Zhenbo School of Information Engineering Zhejiang Ocean University Zhoushan China Zhejiang Ocean University Department of Physics Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9798350372052

In view of the low accuracy and low efficiency of traditional ship target detection methods, this paper introduces an improved ship target detection method based on YOLOv5. Firstly, we preprocess the ship target data set, which includes graph denoising, graph enhancement and so on. Secondly, based on the benchmark YOLOv5 object detection algorithm, SimAM attention mechanism module is introduced. Then, a pyramid feature fusion strategy is added to filter conflicting information in airspace to suppress inconsistent features and improve the network's feature fusion capability for targets of different scales. Finally, the trained model is tested to achieve accurate evaluation of ship target detection. Experimental results show that the proposed ship target detection method is compared with YOLOv5s in accuracy rate, recall rate, mAP@0.5 and mAP@0.5: 0.95 increased by 1.9, 3.8, 2.6 and 7.3 percentage points. Respectively, which can meet the requirements of detection speed and obtain better detection accuracy, effectively realizing high-speed and high-precision ship detection. © 2023 IEEE.

关键词： Object detection

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A Dynamic Cross-Scale Transformer with Dual-Compound Representation for 3D Medical Image Segmentation 48

A Dynamic Cross-Scale Transformer with Dual-Compound Represe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Ruixia Wang, Zhiqiong Wang, Zhongyang Xin, Junchang Northeastern University College of Medicine and Biological Information Engineering China Northeastern University School of Computer Science & Engineering China Northeastern University Key Laboratory of Big Data Management and Analytics Liaoning Province Shenyang China

ISBN: (纸本)9781728163277

Transformer models exploit multi-head self-attention to capture long-range information. Further, window-based self-attention solves the problem of quadratic computational complexity and provides a new solution for dense prediction of 3D images. However, Transformers miss structural information due to the naive tokenization scheme. Furthermore, single-scale attention fails to achieve a balance between feature representation and semantic information. Aiming at the above problems, we propose a window-based dynamic crossscale cross-attention transformer (DCS-Former) for precise representation of the diversity features. DCS-former first constructs dual-compound feature representations through Antehoc Structure-aware Module and Post-hoc Class-aware Module. Then, the bidirectional attention structure is designed to interactively fuse structural features with class representations. The experimental results show that our method outperforms various competing segmentation models on three different public datasets. © 2023 IEEE.

关键词： Deep Learning Medical Image Segmentation Self-attention Vision Transformer

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