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International Conference on Agro-Geoinformatics

作者： Dandan Guo Guangming Li Shuai Wang School of Mechanical Electrical and Information Engineering Shandong University Weihai Shandong P. R. China Shandong Provincial Key Laboratory of Computer Networks National Supercomputer Center in Jinan Jinan Shandong P. R. China

With the wide application of Unmanned Aerial Vehicles (UAVs) in remote sensing, the magnitude of the images obtained by UAVs has increased dramatically. How to manage and utilize those massive images effectively is challenging work. In order to provide a basic platform for agricultural analysis, this study aims to develop a systematical framework for efficient storage and web-based interactive display of UAV remote sensing data. In the framework, we deal with the massive tile-data with high performance clusters based on distributed storage and then establish an effective cache mechanism. Meanwhile, *** is used to develop the system's backend, as its event-driven asynchronous IO model is beneficial to concurrence. Additionally, based on the rich-client technology, the application services such as dynamic combination of multi-spectral images are completed in the browser to relieve the pressure of the server. This framework is easy to migrate to the cloud platform, which shows advantages of high storage efficiency, short data query time, strong scalability, and high concurrency performance.

关键词： Remote sensing Servers Unmanned aerial vehicles Data visualization Spatial databases Throughput

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MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data

arXiv

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arXiv 2018年

作者： Deng, Yang Li, Yaliang Shen, Ying Du, Nan Fan, Wei Yang, Min Lei, Kai School of Electronics and Computer Engineering Peking University PCL Research Center of Networks and Communications Peng Cheng Laboratory Chinese University of Hong Kong Alibaba Group Tencent Medical AI Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. For example, symptom runny nose highly indicates the existence of disease whooping cough when the patient is a baby rather than the people at other ages. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Medical computing

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Research and Implementation of a Software Online Testing Platform Model Based on Cloud Computing 5

Research and Implementation of a Software Online Testing Pla...

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5th International Conference on Advanced Cloud and Big Data, CBD 2017

作者： Chen, Jing Wang, Chunxiao Liu, Fei Wang, Yinglong Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Computing Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9781538610725

In a traditional process of software testing, an user have to buy servers and test tools to set up various test environment artificially, which causes high-cost, long test time and difficult test implements with limited resource. Cloud computing integrates a large number of physical resources into a pool and realizes the virtualization of various resources based on virtualization techniques. Users can achieve various virtual resources on demand from cloud platform without any investment on infrastructures. This offers a new solution to the problems in traditional software testing. So this paper proposes a multi-layer model of a software online testing platform based on cloud computing and implements the test platform. This test platform that include IaaS and SaaS platforms, self-help service and operation maintenance portals realizes automatic creation of test environments, remote use of test tools and online test services. Users can get various test services on demand only logging in to this platform at anytime and anywhere, which has the advantage of low test cost and high efficiency. In addition, this platform is also tested in terms of resource provision time and load variation, which shows the ability of this platform to satisfy users' resource demands. © 2017 IEEE.

关键词： Software testing

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A Trend Forecast of Import and Export Trade Total Volume based on LSTM

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IOP Conference Series: Materials Science and engineering 2019年第1期646卷

作者： Qinglin Qu Zhao Li Juanjuan Tang Shiwei Wu Ruishuang Wang Shandong Business Information Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Big Data Engineering Technology Research Center of E-Government Shandong

The monthly import and export data are usually with the challenging characteristics including large scale, nonlinear and hard to fit, leading to their development trends can be hardly predicted. To tackle this problem, we propose to leverage the LSTM-based recurrent neural network to forecast the development trend in this paper. We demonstrate the effectiveness of our approach based on the monthly import and export data of Shandong Province from January 2001 to June 2018. In particular, we achieve the MSE score of 124.39, which outperforms the traditional time series model less than 12.2%.

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Search for axion-like dark matter using solid-state nuclear magnetic resonance

arXiv

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arXiv 2021年

作者： Aybas, Deniz Adam, Janos Blumenthal, Emmy Gramolin, Alexander V. Johnson, Dorian Kleyheeg, Annalies Afach, Samer Blanchard, John W. Centers, Gary P. Garcon, Antoine Engler, Martin Figueroa, Nataniel L. Sendra, Marina Gil Wickenbrock, Arne Lawson, Matthew Wang, Tao Wu, Teng Luo, Haosu Mani, Hamdi Mauskopf, Philip Graham, Peter W. Rajendran, Surjeet Jackson Kimball, Derek F. Budker, Dmitry Sushkov, Alexander O. Department of Physics Boston University BostonMA02215 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Helmholtz-Institut GSI Helmholtzzentrum für Schwerionenforschung Mainz55128 Germany Johannes Gutenberg-Universität Mainz Mainz55128 Germany The Oskar Klein Centre for Cosmoparticle Physics Department of Physics Stockholm University AlbaNova Stockholm10691 Sweden Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 Stockholm10691 Sweden Department of Physics Princeton University PrincetonNJ08544 United States State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Center for Quantum Information Technology Peking University Beijing100871 China Shanghai Institute of Ceramics Chinese Academy of Sciences China School of Earth and Space Exploration Arizona State University TempeAZ85287 United States Stanford Institute for Theoretical Physics Stanford University StanfordCA94305 United States Department of Physics & Astronomy The Johns Hopkins University BaltimoreMD21218 United States Department of Physics California State University East Bay HaywardCA94542-3084 United States Department of Physics University of California BerkeleyCA94720-7300 United States Photonics Center Boston University BostonMA02215 United States

We report the results of an experimental search for ultralight axion-like dark matter in the mass range 162 neV to 166 neV. The detection scheme of our Cosmic Axion Spin Precession Experiment (CASPEr) is based on a precision measurement of 207Pb solid-state nuclear magnetic resonance in a polarized ferroelectric crystal. Axion-like dark matter can exert an oscillating torque on 207Pb nuclear spins via the electric-dipole moment coupling gd, or via the gradient coupling gaNN. We calibrated the detector and characterized the excitation spectrum and relaxation parameters of the nuclear spin ensemble with pulsed magnetic resonance measurements in a 4.4 T magnetic field. We swept the magnetic field near this value and searched for axion-like dark matter with Compton frequency within a 1 MHz band centered at 39.65 MHz. Our measurements place the upper bounds |gd| −4 GeV−2 and |gaNN| −1 GeV−1 (95% confidence level) in this frequency range. The constraint on gd corresponds to an upper bound of 1.0 × 10−21 e · cm on the amplitude of oscillations of the neutron electric dipole moment, and 4.3×10−6 on the amplitude of oscillations of CP-violating θ parameter of quantum chromodynamics. Our results demonstrate the feasibility of using solid-state nuclear magnetic resonance to search for axion-like dark matter in the nanoelectronvolt mass range. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum theory

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Context-aware group buying in ultra-dense small cell networks: Unity is strength

arXiv

引用

arXiv 2018年

作者： Zhang, Yuli Xu, Yuhua Anpalagan, Alagan Wu, Qihui Xu, Yitao Sun, Youming Feng, Shuo Luo, Yunpeng College of Communications Engineering Army Engineering University of the PLA Nanjing210007 China Science and Technology on Communication Networks Laboratory Shijiazhuang050002 China Department of Electrical and Computer Engineering Ryerson University Toronto Canada College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Cognitive Systems Laboratory McMaster University HamiltonONL8S 4L8 Canada

The ultra-dense small cell networks (SCNs) have been regarded as a promising technology to solve the data traffic explosion in future. However, the complicated relationships among large scale users and cells practically demand a cooperative grouping mechanism to account for the shortage of resources in SCNs. Then, a group buying market mechanism provides a win-win situation and an effective resource allocation in the view of economics. With additional decision information being provided, the context awareness enhances and improves the group buying mechanism. In this article, we propose a Context-Aware Group Buying (CAGB) mechanism to allocate the resources in ultra-dense SCNs. The feasibility, necessity, and effectiveness of CAGB are analyzed first. Then, we introduce the group buying mechanism and some common context awareness. The relationship between context awareness and group buying is also analyzed. Some important technologies in SCNs are discussed in the view of CAGB, such as load balancing, spectrum management, and cooperative caching. Then, the graphical coalition formation games (CFGs) of CAGB are also presented to match the complicated network topologies in SCNs. Two CAGB-based use cases about spectrum market and cooperative caching are presented. Finally, future research issues about context awareness, grouping mechanism, and buying mechanism are discussed. Copyright © 2018, The Authors. All rights reserved.

关键词： Cytology

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All-Optical 216 Gbps Super-channel Data Switching Using Co-Polarized Pump in SOA

All-Optical 216 Gbps Super-channel Data Switching Using Co-P...

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International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)

作者： A. Ragheb S. Alshebeili M. A. Esmail H. Fathallah KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS) Electrical Engineering Department King Saud University Riyadh Saudi Arabia Communications and Networks Engineering Department Prince Sultan University Riyadh Saudi Arabia Laboratory of physics of materials-structures and properties Computer Department University of Carthage Tunis Tunisia

ISBN: (纸本)9781538691328;9781538691311

Efficient bit transmission, flexible spectrum utilization, polarization and space division multiplexing (PDM and SDM), and advanced laser designs are among the recent technologies adopted for boosting the capacity and spectral efficiency (SE) of optical communication networks. In addition, all-optical wavelength conversion is required to reduce network latency and energy consumption. In this paper, we exploit the modulation and polarization transparency features of the semiconductor optical amplifiers (SOAs) in order to build a photonics-based wavelength converter for single and super-channel quadrature phase shift keying (QPSK)-PDM signals. An experimental demonstration is conducted and high transmission speeds are achieved including 18-, 20-, and 25-Gbaud. The experimental results show a conversion of a super-channel signal consisting of three dual-polarization QPSK signals with 216-Gbps total aggregated data rate using a co-propagated pump four-wave mixing (FWM) configuration.

关键词： Optical wavelength conversion Semiconductor optical amplifiers Optical switches Optical attenuators Optical pumping Optical polarization Optical receivers

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Joint user grouping and resource allocation for uplink virtual MIMO systems

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Science China(Information Sciences) 2016年第2期59卷 60-73页

作者： Xiaofeng LU Kun YANG Wenna LI Shaojun QIU Hailin ZHANG State Key Laboratory of Integrated Service Networks Xidian University School of Computer Science and Electrical Engineering University of Essex

MIMO has become a core technology of 5G network to largely improve system throughput. Due to the cost and size of the user equipment(UE), the application of MIMO uplink is limited by the difficulty in practical implementation at the user side. Virtual MIMO has been widely investigated to solve this problem for wireless uplink systems. However, virtual MIMO transmission leads to performance degradation due to the multiuser interference. To obtain good trade-off between the system throughput and transmission performance,we investigate joint user grouping and resource allocation under the consideration of system throughput and average mean squared error(MSE) performance in SC-FDMA uplink systems. Based on linear MIMO detection,we first develop MSE-oriented user grouping criteria for evaluation of transmission performance, then establish dynamic user grouping and optimal resource allocation problems for hard and elastic average MSE *** proposed joint resource allocation algorithm is evaluated in SC-FDMA uplink scenarios and the results show that it achieves maximum system throughput with average MSE guaranteed for the hard MSE constraint algorithms and the alterable trade-off between system throughput and average MSE for the elastic MSE constraint algorithms.

关键词： virtual MIMO user grouping average MSE system throughput resource allocation

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$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

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IEEE Transactions on Information Forensics and Security 2018年第9期13卷 2151-2166页

作者： Xuebin Ren Chia-Mu Yu Weiren Yu Shusen Yang Xinyu Yang Julie A. McCann Philip S. Yu School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Taiwan Information Security Center Taipei Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan Department of Computing Imperial College London London U.K. School of Engineering and Applied Science Aston University Birmingham U.K. Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Department of Computer Science University of Illinois at Chicago Chicago IL USA

High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm (LoPub) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.

关键词： Privacy Data privacy Servers Correlation Estimation Frequency estimation Sensors

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Single-drive high-speed lumped depletion-type modulators toward 10 fJ/bit energy consumption

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Photonics Research 2017年第2期5卷 134-142页

作者： XINBAI LI FENGHE YANG FANG ZHONG QINGZHONG DENG JURGEN MICHEL ZHIPING ZHOU State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking University MIT Microphotonics Center Massachusetts Institute of Technology

Reduction of modulator energy consumption to 10 fJ∕bit is essential for the sustainable development of communication *** modulators might be a viable solution if instructed by a complete theory ***,we present a complete analytical electro-optic response theory,energy consumption analysis,and eye diagrams on absolute scales for lumped *** the speed limitation is understood and alleviated by single-drive configuration,and comprehensive knowledge into the energy dependence on structural parameters significantly reduces energy *** results show that silicon modulation energy as low as 80.8 and 21.5 fJ∕bit can be achieved at 28 Gbd under 50 and 10 Ω impedance drivers,respectively.A 50 Gbd modulation is also shown to be *** analytical models can be extended to lumped modulators on other material platforms and offer a promising solution to the current challenges of modulation energy reduction.

关键词： length type high

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