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A Novel Multi-Party Authentication Scheme for FCN-based MIoT systems in Natural Language Processing Environment

ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Xiangwei Meng Ce Yang Yiren Qi Wei Liang Zisang Xu Kuanching Li Hai Deng Nanjing University of Aeronautics and Astronautics China Hunan University of Science and Technology China Columbia University USA Hunan University of Science and Technology and Hunan Key Laboratory for Service computing and Novel Software Technology China Changsha University of Science and Technology China Florida International University USA

Natural language processing (NLP) assists to increase the efficiency of human and Multimedia Internet of Things (MIoT) interaction. Notably, large-scale NLP tasks can be offloaded from a cloud server to fog nodes closer to a mobile terminal device for lower response latency. But communication security is ongoing issues that need to be addressed. Effective mutual authentication among multiple entities is essential to ensure the security of MIoT systems based on a dynamic Fog computing Network (FCN). However, the existing schemes are unsuitable for the dynamic FCN due to the security vulnerabilities such as the linkable sessions. To solve this problem, an Anonymous Multi-Party Authentication (AMPA) scheme is proposed to address the challenges of secure FCN-based MIoT communications in this paper. The proposed scheme uses a bilinear pairing operation to realize the authentication between the fog nodes and cloud server and to establish the group key. Besides, the scheme allows cloud-authenticated terminal devices to be added to the FCN and reduces the need for the resource-limited terminal device to perform many authentication protocols. The security analysis is carried out to demonstrate that AMPA scheme can meet various safety requirements. Performance evaluations shown that the proposed AMPA scheme achieves satisfactory performance.

关键词： Multimedia Internet of Things natural language processing fog computing network authentication protocol

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A Low-Resolution Video Action Recognition Approach Based on Multi-Scale Reconstruction and Multi-Modal Fusion

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IEEE Transactions on Consumer Electronics 2024年

作者： Zheng, Hui Zhao, Yesheng Zhang, Bo Shang, Guoqiang Al-Shamri, Mohammad Yahya H. Aldossary, Haya Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences Hefei Institute of Technology Innovation and Engineering Hefei230094 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Non Commissioned Officer School of Space Engineering University Beijing101416 China King Khalid University School of Computer Science Abha36311 Saudi Arabia Imam Abdulrahman Bin Faisal University Department of Computer Science Jubail31961 Saudi Arabia

The challenge of low-resolution video action recognition task lies in recovering and extracting feature representations that can effectively capture action characteristics with limited semantic information. In this paper, we propose an approach to address this challenge, which primarily comprises a multi-scale reconstruction module and a multi-modal fusion module. In multi-scale reconstruction module, we introduce a frequency-adaptive reconstruction model to reconstruct lost information from multiple scales. For crucial high-frequency sub-band images, we propose a wavelet-based super-resolution generative adversarial network to recover detailed information. In multi-modal fusion module, we propose a two-stream Transformer-based network to mine spatial-temporal joint feature representations from the reconstructed video. Additionally, we utilize another Transformer model to fuse features from different modalities, capturing both consistent and complementary representations. Finally, the fused features are fed into a classifier for recognition. Experimental results show that our proposed model outperforms other models for low-quality action recognition on HMDB51 (16×12 58.70%, 14×14 62.25%,80×60 68.94%), UCF101 (14×14 76.74%, 28×28 84.15%, 80×60 92.78%), and Tiny-VIRAT (35.63%) datasets. © 1975-2011 IEEE.

关键词： Image reconstruction

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Quantifying the impact of scholarly papers based on higher-order weighted citations

arXiv

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arXiv 2020年

作者： Bai, Xiaomei Zhang, Fuli Hou, Jie Lee, Ivan Kong, Xiangjie Tolba, Amr Xia, Feng Computing Center Anshan Normal University Anshan114007 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Library Anshan Normal University Anshan114007 China School of ITMS University of South Australia Mawson LakesSA5095 Australia Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia Mathematics Department Faculty of Science Menoufia University Shebin-El-Kom32511 Egypt

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical. Copyright © 2020, The Authors. All rights reserved.

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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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Science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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Ensuring Privacy and Correlation Awareness in Multi-Dimensional service Quality Prediction and Recommendation for Iot

SSRN

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SSRN 2024年

作者： Zhong, Weiyi Fang, Wei Zhao, Yifan Wang, Sifeng Yan, Chao Jiang, Rong Khan, Maqbool Yang, Xuan Rafique, Wajid Qufu Normal University School of Engineering Rizhao276800 China Qufu Normal University School of Computer Science Rizhao276800 China Weifang University of Science and Technology Weifang262700 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650000 China Software Competence Center Hagenberg Upper Austria 4232 Austria Angeles University Foundation Pampanga2009 Philippines University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada

Edge computing, with its advantages in terms of lightweight data transmission between users and cloud platforms, has become a promising solution for alleviating the heavy burden of timely data processing in many IoT scenarios, such as smart commerce and wise medicine. While several challenges are raised when we fuse the multiple-source IoT data recorded by different edge servers. First of all, data repetition within each edge server can greatly reduce the efficiency of various edge-based smart applications. Besides, IoT data fusion associated with multiple distributed edge servers can compromise user privacy. In addition, the multi-dimensional and interrelated nature of IoT data complicates precise data mining and analysis. To tackle these issues, a novel edge data fusion method (named TLTM) for cross-platform service recommendation is brought forth, which considers data dimensions, data correlation, and data privacy simultaneously. At last, to validate the effectiveness and efficiency of the TLTM method, we have designed massive experiments on the popular WS-DREAM dataset. The reported experimental results show that our TLTM method is superior to other related methods in terms of popular performance metrics including MAE, RMSE, Precision, Recall, F1-Score, and Time cost. © 2024, The Authors. All rights reserved.

关键词： Quality of service

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Unified Rendering Array Driver Runtime Optimization for Graphic Application

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IOP Conference Series: Earth and Environmental Science 2019年第1期234卷

作者： Chengcheng Ma Ze Tian Honghong Liu Zhao Nie China Aeronautical Computing Research Institute Aviation key Laboratory of Science and Technology on integrated Circuit and Micro-System Design P.O. Box 710068 China Xiangteng microelectronics technology co. LTD P.O. Box 710068 China

As the core of GPU processing, the performance of Unified rendering array is directly affected by the shader driver. On the basis of research the structure of Unified rendering array processing, this paper proposes a runtime Unified rendering array driver optimization method for graphical application, can according to different graphics application scenario runtime to generate optimized Unified rendering array driver, testing on graphics processors FPGA verification environment, driven by the runtime optimization compared with the traditional full function driver, performance increased by 55% - 270%.

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Distance Measurement system Based on Binocular Stereo Vision

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IOP Conference Series: Earth and Environmental Science 2019年第5期252卷

作者： Xiyan Sun Yingzhou Jiang Yuanfa Ji Wentao Fu Suqing Yan Qidong Chen Baoguo Yu Xingli Gan Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin 541004 China National & Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin 541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Guilin 541004 China Key laboratory of radio wave environment and modeling technology Qingdao 266000 China State key laboratory of satellite navigation system and equipment technology Shijiazhuang 050000 China

Binocular stereo vision uses two cameras of the same specification instead of human eyes for measurement and judgment. The basic principle of binocular vision ranging is to simultaneously capture the image of the measured object by using two left and right cameras. The objectmeasuredisdifferent, andthe positions under the different camera screensaredifferent, this difference in position is called disparity. The three-dimensional information of the object is obtained according to the principle of disparity. The binocular stereo vision ranging system, which combines the advantages of high efficiency and high precision, is ideal for non-contact detection and quality control. This paper proposes a method combining MATLAB calibration and OpenCV matching to achieve binocular stereo vision. Using the MATLAB calibration tool to perform stereo calibration of the camera, combined with OpenCV for stereo rectification and stereo matching, the disparity value is obtained, and the distance from the target to the camera is calculated by disparity. Simulations show that this method can satisfy the distance measurement.

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Amorphous alloys surpass E/10 strength limit at extreme strain rates

arXiv

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arXiv 2021年

作者： Zhu, Wenqing Li, Zhi Shu, Hua Gao, Huajian Wei, Xiaoding State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China School of Mechanical and Aerospace Engineering College of Engineering Nanyang Technological University 70 Nanyang Drive 637457 Singapore Shanghai Institute of Laser Plasma China Academy of Engineering Physics Shanghai201102 China Institute of High Performance Computing A*STAR 138632 Singapore Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing100871 China Peking University Nanchang Innovation Institute Nanchang330000 China

Theoretical predictions of the ideal strength of materials range from E/30 to E/10 (E is Young’s modulus). However, despite intense interest over the last decade, the value of the ideal strength that can be attained experimentally for metals remains a mystery (1-5). In this study, we demonstrated the unprecedented strength of an amorphous Cu-Zr alloy that surpassed the E/10 limit. Laser-induced shock experiments were conducted on Cu50Zr50 to explore its strength and failure mechanisms at ultrahigh strain rates. The material demonstrated a high spall strength of 9.8 GPa, approximately 1/13 of its P-wave modulus (~ E/6), at strain rates greater than 107 s−1, which sets a new record for the elastic limit of metallic materials. Electron microscopy and large-scale molecular dynamics simulations revealed that void nucleation and growth, not shear-banding, comprised the major failure mechanism for metallic glasses at extremely fast strain rates. A new model for void formation under the control of surface energy explained the rate dependence of the material strength. The results of this study reveal new possible ways to use the amorphous phase in nanostructured metals in future applications under demanding mechanical conditions. Copyright © 2021, The Authors. All rights reserved.

关键词： Copper alloys

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NTIRE 2020 Challenge on Image and Video Deblurring

arXiv

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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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Deep Stable Learning for Cross-lingual Dependency Parsing

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Haijiang Liu Chen Qiu Qiyuan Li Maofu Liu Li Wang Jinguang Gu Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Scientific and Technical Information of China Beijing China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing China

The Cross-lingual Dependency Parsing (XDP) task poses a significant challenge due to the differences in dependency structures between training and testing languages, known as the out-of-distribution (OOD) problem. Our research delved into this issue in the XDP dataset by selecting 43 languages from 22 language families. We found that the primary factor of the OOD problem is the unbalanced length distribution among languages. To address the impact of the OOD problem, we propose deep stable learning for Cross-lingual Dependency Parsing (SL-XDP), which utilizes deep stable learning with a feature fusion module. In detail, we implemented five feature fusion operations for generating comprehensive representations with dependency relations and the deep stable learning algorithm to decorrelate dependency structures with sequence length. Our experiments on Universal Dependencies have demonstrated that SL-XDP can lessen the impact of the OOD problem and improve the model generalization among 21 languages, with a maximum improvement of 18%.

关键词： Cross-lingual Dependency Parsing Deep Stable Learning Out-of-Distribution Problem

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