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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： xiao Zhang Binbin Liu Zian Gou Jia Shi xiaonan Zhao School of Computer Science Northwestern Polytechnical University 127 Youyi xi Road Xi’an Shaanxi China Key Laboratory of Big Data Storage and Management of MIIT in NWPU National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology in NWPU

ISBN: (纸本)9781728176505

Hadoop Distributed File System (HDFS) is an important component of Hadoop, which provides data storage service. The performance of the IO subsystem has a great influence on data processing efficiency. In the Hadoop system, computing jobs are scheduled to the nodes where the data is located to reduce IO time. The job scheduler and data distribution have a great impact on the efficiency of data processing. HDFS provides a mechanism for users to specify files or directories to be cached. But the cached data is only accessible for the jobs running in the same data nodes. In this paper, we present a distributed cache mechanism for HDFS with RDMA-capable network. We separate the caching function from the original data nodes. We design and implement a new kind of component for the caching function. The cached data in these nodes can be accessed directly by any nodes in the cluster through Remote Direct Memory Access (RDMA). Compared with the existing caching mechanism, our approach improves the performance and stability of the IO process. We also accelerate the data writing process using our caching mechanism. The experiments show that compared with the existing caching mechanism of HDFS, our scheme improves the reading latency up to 3 times, improves the throughput by 30%~95% for large files, and the writing performance by 64%. When the task runs on different nodes, the IO fluctuation caused by different data distribution is dropped from 20.71 to 0.50.

关键词： Fluctuations File systems High performance computing Conferences Memory Writing Throughput

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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science China(Information sciences) 2021年第1期64卷 5-78页

作者： xiaohu YOU Cheng-xiang WANG Jie HUANG xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG xiaofeng TAO Shaoqian LI Zhi CHEN xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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International Conference on Networking and Network Applications (NaNA)

作者： Zhongmin Wang Rui Zhou Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an Shaanxi China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

The information interaction when the brain processes emotional activities is intricate. Therefore, it is very necessary for us to explore the mechanisms of the functional coordination of various brain regions. Recent studies have demonstrated that there are hub regions in the brain that are highly connected and highly central, and are responsible for coordinating dynamic interactions between brain regions. This study aims to construct the dynamic brain networks related to emotions and examine the structure of the hubs of these networks that reflect functional connectivity. In this paper, we demonstrate that a combination of network hubs set a closely connected collective called “rich-club”,which can be used as the core network architecture for identifying different emotions. According to the phase synchronization relationship of the Electroencephalogram (EEG) in three time periods to construct the corresponding brain networks in different sub-states, we found that the dynamic connection patterns produced by different emotions are different, which is manifested in the different rich-club organization. The discovery of rich-club structure related to emotions in this study is of great significance for studying the brain mechanisms behind human emotional processes.

关键词： Emotion recognition Organizations Color Network architecture Electroencephalography Synchronization

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Distance variety preserving hashing for large-scale retrieval

Distance variety preserving hashing for large-scale retrieva...

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作者： Zhai, Sheping School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an710121 China

Hashing techniques have been seen huge adoption in large-scale retrieval owing to the computational and storage efficiencies of binary codes. However, the binary codes might not preserve the semantic similarity powerfully and lead to poor accuracy. To evade binary limitation but remain hashing superiority, this paper introduces a novel asymmetric hashing framework for distance variety preserving, which yields preferable performance via real-valued query and multi-valued database with shorter code length. Concretely, a binary sparse representation is proposed for multivalued database encoding to make computation and storage complexity tolerable. By exploiting the supervised information, we learn a projective matrix that project data onto a sub-space in which the similarity between realvalued query and multi-valued database can be measured. Due to the discrete constraints, for different discrete variables, we present different meticulous discrete solutions for their sub-problems. Exhaustive experiments on three multilabel datasets demonstrate our approach outperforms most state-of-the-art hashing methods. © 2018 John Wiley & Sons, Ltd.

关键词： database systems

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Multi-Modal Transformer for Accelerated MR Imaging

arXiv

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arxiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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MAVEN-ERE: A Unified Large-scale dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

arXiv

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arxiv 2022年

作者： Wang, xiaozhi Chen, Yulin Ding, Ning Peng, Hao Wang, Zimu Lin, Yankai Han, Xu Hou, Lei Li, Juanzi Liu, Zhiyuan Li, Peng Zhou, Jie Department of Computer Science and Technology BNRist China Shenzhen International Graduate School China THU-Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT China Tsinghua University Beijing China Xi’an Jiaotong-Liverpool University Suzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Pattern Recognition Center WeChat AI Tencent Inc China

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://***/THU-KEG/MAVEN-ERE. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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Improved siamese network for video-based person re-identification

Improved siamese network for video-based person re-identific...

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2019 International Conference on Networking and Network Applications, NaNA 2019

作者： Wang, Zhongmin Cai, Lanlan Duan, Na Fan, Lin Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'An University of Posts and Telecommunications Xi'an shaanxi710121 China School of Computer Science and Technology Xi'An University of Posts and Telecommunications Xi'an shaanxi710121 China

ISBN: (纸本)9781728126296

In the field of human-computer interaction and visual surveillance, person re-identification is undoubtedly a key task. In this paper, we propose improved Siamese network for video-based person re-identification, which combines the verification model and the classification model, in a way that all the supervision information of the samples is fully utilized. The key frame extraction is able to select informative frames over the sequence, which removes occluded frames according to optical flow vector and measures the similarity of two adjacent frames' contents in terms of the block mutual information entropy. A convolutional network and long short-term memory joint training is used as a feature extractor to re-identify using the improved Siamese network framework. Experimental data on the iLIDS-VID and PRID-2011 datasets show that this approach is superior to existing video-based re-identification methods. © 2019 IEEE.

关键词： Optical flows

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Emotional analysis based on dynamic functional brain network

Emotional analysis based on dynamic functional brain network

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2019 International Conference on Networking and Network Applications, NaNA 2019

作者： Wang, Zhongmin Tong, Yue Heng, xia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'An University of Posts and Telecommunications Xi'an Shaanxi710121 China School of Computer Science and Technology Xi'An University of Posts and Telecommunications Xi'an Shaanxi710121 China

ISBN: (纸本)9781728126296

The human brain dynamically changes in the second and millisecond levels, so constructing a functional brain network using static connections can result in the absence of some time-related effective features. In this paper, a dynamic network model construction method is proposed. The basic idea of the proposed dynamic emotion analysis method is to construct different network modes based on the channel correlation mode and band power distribution method, and use the sliding window method to estimate the dynamic correlation mode and dynamic power distribution changes, Then the multivariate features of network-level brain dynamics are extracted and classified. We conducted experiments on the SEED database to study network-level dynamic transformations in the process of positive, neutral, and negative emotions. The results verify the feasibility of the emotional assessment method based on dynamic functional connection, and provide a new way to establishing brain dynamic model under different emotional states. The results show that emotion analysis based on dynamic networks is more accurate than static networks. © 2019 IEEE.

关键词： Band power Brain function network Channel correlation mode Dynamic emotion analysis Sliding window

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An improved person reidentification method by selective convolutional descriptor aggregation

An improved person reidentification method by selective conv...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Zhou, Shuren Ke, Maolin Luo, Peng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9783319987750

Person re-identification (person re-ID) is refers to pedestrians matching under a multi camera network in a non-overlapping field of view, that is, whether the pedestrian targets taken by different cameras are the same. Therefore, person re-ID is also a sub problem of image retrieval. In this paper, fine grained image retrieval method SCDA (Selective Convolutional Descriptor Aggregation) is used in person re-ID, and the framework of identification-verification is improved. The method can select useful deep descriptors, at the same time, remove background by localizing the main object. © Springer Nature Switzerland AG 2019.

关键词： computer vision

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A survey of multi-object video tracking algorithms

A survey of multi-object video tracking algorithms

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Zhou, Shuren Ke, Maolin Qiu, Jia Wang, Jin Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9783319987750

Video multi-object tracking is one of the important research topics in the field of computer vision, which is widely used in military and civil areas. At present, the research of single object tracking algorithm is quite mature, however the research of multi-object tracking is still ongoing. This paper focuses on four important stages in the multi-object tracking process: feature extraction, detector, data association and the tracker. The feature extraction part introduces the current methods of feature extraction, as well as the merits and demerits of each method;In the stage of detection, the tracking effect of the object appearance model in specific applications is described, and then the paper analyze the multi-object tracking algorithm based on detection and tracking as well as the multi-object tracking algorithm based on deep learning;In the tracking stage, the establishment of object motion model and multi-object tracking with different tracker hybrid algorithm are introduced;During the stage of data association, the paper introduce the multi-object tracking based on energy minimization and commonly used data association algorithm, respectively. Then the current mainstream datasets and evaluation methods are introduced. Finally, the future development of the multi-object tracking is discussed and forecasted. © Springer Nature Switzerland AG 2019.

关键词： Feature extraction

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