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6th CCF Academic Conference on Big data, CCF Big data 2018

作者： Li, Jingyi Wu, Jigang Chen, Long Li, Jiaxing Guangdong University of Technology Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9789811329210

Secure deduplication techniques have been wildly used in cloud storage to save both disk space and network bandwidth. However, traditional schemes store only one copy in cloud which raises problems with data reliability. Meanwhile, the existence of any third party, which is adopted by many deduplication schemes, will bring the risk of being single-pointed. In order to solve the above problem, this paper proposes a deduplication scheme which distributes files to multi servers and records storage informations on blockchain. We present smart contract based protocols without the participants of the central authorities to provide secure deduplication. With safety analysis, we demonstrate that our deduplication scheme is secure and reliable under the definitions specified in the proposed security model with acceptable performance through simulation experiments. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Blockchain

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Combining deep learning and topic modeling for review understanding in context-Aware recommendation

Combining deep learning and topic modeling for review unders...

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2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018

作者： Jin, Mingmin Luo, Xin Zhu, Huiling Zhuo, Hankz Hankui School of Data and Computer Science Sun Yat-Sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9781948087278

With the rise of e-commerce, people are accustomed to writing their reviews after receiving the goods. These comments are so important that a bad review can have a direct impact on others buying. Besides, the abundant information within user reviews is very useful for extracting user preferences and item properties. In this paper, we investigate the approach to effectively utilize review information for recommender systems. The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding. In the experiments on popular review dataset Amazon , our LTMF model outperforms previous proposed HFT model and ConvMF model in rating prediction. Furthermore, LTMF shows the better ability on making topic clustering than traditional topic model based method, which implies integrating the information from deep learning and topic modeling is a meaningful approach to make a better understanding of reviews. © 2018 The Association for Computational Linguistics.

关键词： Factorization

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A skeleton pattern representation method for anomaly detection in wireless sensor networks 21

A skeleton pattern representation method for anomaly detecti...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Yanping Yang, Ping Gao, Cong Wang, Zhongmin Yu, Zhong 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'an China School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728120584

Anomaly detection of time series in wireless sensor networks has gained significant attention. Researchers employ representation techniques to reduce the dimensionality of time series. The Piecewise Aggregate Pattern Representation (PAPR) method vertically divides the time series and represents as patterns. However, the computational cost of the PAPR method for anomaly detection is high. To overcome this issue, we propose a time series representation method for anomaly detection called Skeleton Pattern Representation for anomaly detection (SPR-AD). An improved Perceptually Important Point (iPIP) identification algorithm is devised to transform the subsequence into a skeleton with PIPs. Then the skeleton is divided into several equal-sized subspaces in the amplitude domain. Statistical information of the PIPs located in subspaces is constructed to represent the skeleton as a pattern. Based on the two-phase representation scheme, anomaly is determined by anomaly scores which are based on the of similarity among pattern. The experimental results demonstrate the performance of the SPR-AD method is better in terms of computational cost and detection accuracy than the PAPR method. © 2019 IEEE.

关键词： Anomaly detection

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Multi-Adapter RGBT Tracking

Multi-Adapter RGBT Tracking

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Cheng Long Li Andong Lu Ai Hua Zheng Zhengzheng Tu Jin Tang Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.

关键词： Adaptation models Robustness Target tracking Feature extraction Visualization Task analysis

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On the Connectivity of Highly Dynamic Wireless Sensor Networks in Smart Factory

On the Connectivity of Highly Dynamic Wireless Sensor Networ...

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International Conference on Networking and Network Applications (NaNA)

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

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

With the development of smart manufacturing in Industry 4.0, large amount of heterogeneous data are generated from multiple sources. Various data processing techniques can be applied to these data for the purpose of extracting potential information about the status of the manufacturing network. Thus, data are valuable to the analysis and adjustment of the manufacturing processes. Due to the diversity of products, assembly lines and manufacturing processes are highly dynamic. In view of the flexible deployment of wireless sensor networks, they have been widely deployed to conduct data collection. However, due to the limited radio range of a wireless sensor node, the connectivity of a highly dynamic wireless sensor network is a key issue. In this paper, we analyzed the connectivity in a wireless sensor network and accorded three types of connectivity. Then, we developed two network connectivity models from the perspective of one dimension and two dimensions. The degrees of connectivity for both two nodes and the network are elaborated. Finally, the relation between the number of sink nodes and the average degree of connectivity of the network is analyzed based on experimental results.

关键词： Wireless networks Wireless sensor networks Euclidean distance Smart manufacturing Analytical models

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Ligand Modulation of Active Sites to Promote Cobalt-Doped 1T-MoS2Electrocatalytic Hydrogen Evolution in Alkaline Media

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Angewandte Chemie 2023年第48期135卷

作者： Hai-Jun Liu Shuo Zhang Prof. Yong-Ming Chai Prof. Bin Dong State Key Laboratory of Heavy Oil Processing College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao 266580 P. R. China Contribution: Data curation (lead) Formal analysis (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Data curation (supporting) Formal analysis (supporting) Methodology (supporting) Writing - original draft (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (equal) Data curation (supporting) Formal analysis (supporting) Funding acquisition (equal) Investigation (equal) Methodology (equal) Project administration (equal) Resources (equal) Supervision (equal) Writing - original draft (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (equal) Data curation (supporting) Formal analysis (supporting) Funding acquisition (equal) Methodology (equal) Project administration (equal) Resources (equal) Supervision (equal) Writing - original draft (supporting) Writing - review & editing (supporting)

Highly efficient hydrogen evolution reaction (HER) electrocatalyst will determine the mass distributions of hydrogen-powered clean technologies, while still faces grand challenges. In this work, a synergistic ligand modulation plus Co doping strategy is applied to 1T−MoS 2 catalyst via CoMo-metal-organic frameworks precursors, boosting the HER catalytic activity and durability of 1T−MoS 2 . Confirmed by Cs corrected transmission electron microscope and X-ray absorption spectroscopy, the polydentate 1,2-bis(4-pyridyl)ethane ligand can stably link with two-dimensional 1T−MoS 2 layers through cobalt sites to expand interlayer spacing of MoS 2 (Co−1T−MoS 2 -bpe), which promotes active site exposure, accelerates water dissociation, and optimizes the adsorption and desorption of H in alkaline HER processes. Theoretical calculations indicate the promotions in the electronic structure of 1T−MoS 2 originate in the formation of three-dimensional metal-organic constructs by linking π-conjugated ligand, which weakens the hybridization between Mo-3d and S-2p orbitals, and in turn makes S-2p orbital more suitable for hybridization with H-1s orbital. Therefore, Co−1T−MoS 2 -bpe exhibits excellent stability and exceedingly low overpotential for alkaline HER (118 mV at 10 mA cm −2 ). In addition, integrated into an anion-exchange membrane water electrolyzer, Co−1T−MoS 2 -bpe is much superior to the Pt/C catalyst at the large current densities. This study provides a feasible ligand modulation strategy for designs of two-dimensional catalysts.

关键词： Cobalt Doping Enlarged Interlayer Space Hydrogen Evolution Reaction Ligand Modulation Molybdenum Sulfide

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Trust-based distributed kalman filter estimation fusion under malicious cyber attacks 21

Trust-based distributed kalman filter estimation fusion unde...

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作者： Chen, Yanping Ma, Long Xia, Hong Gao, Cong Wang, Zhongmin Yu, Zhong School of Computer Science AND Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shananxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an710121 China School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728120584

We consider distributed Kalman filter for dynamic state estimation over wireless sensor networks. It is promising but challenging when network is under cyber attacks. Since the information exchange between nodes, the malicious attacks quickly spread across the entire network, which causing large measurement errors and even to the collapse of sensor networks. Aiming at the malicious network attack, a trust-based distributed processing frame is proposed. Which allows neighbor nodes to exchange information, and a series of trusted nodes are found using truth discovery. As a demonstration, distributed Cooperative Localization is considered, and numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks. © 2019 IEEE.

关键词： Kalman filters

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A recurrent Gaussian process regression model with composite kernel for industrial process quality prediction

A recurrent Gaussian process regression model with composite...

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2019 International Conference on Networking and Network Applications, NaNA 2019

作者： Chen, Yanping Gao, Cong Yang, Weihui Wang, Zhongmin Yu, Zhong Xia, Hong 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 School of Communication and Information Engineering Xi'An University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728126296

With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an industrial process. To facilitate the description of the data characteristics in an industrial process, the kernel function is reconstructed with respect to different kinds of data. We propose a novel structure based on the Gaussian process regression model which incorporates the previously predicted values into the input data of subsequent prediction. A series of contrastive experiments are conducted on the Tennessee Eastman process simulator. Experimental results show that the proposed method possesses a higher prediction accuracy than several notable prediction methods. © 2019 IEEE.

关键词： Forecasting

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Local averaging type a posteriori error estimates for the nonlinear steady-state Poisson-Nernst-Planck equations

arXiv

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arXiv 2020年

作者： Yang, Ying Shen, Ruigang Fang, Mingjuan Shu, Shi School of Mathematics and Computational Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China School of Mathematics and Computational Science Guilin University of Electronic Technology GuilinGuangxi541004 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China

The a posteriori error estimates are studied for a class of nonlinear stead-state Poisson-Nernst-Planck equations, which are a coupled system consisting of the Nernst-Planck equation and the Poisson equation. Both the global upper bounds and the local lower bounds of the error estimators are obtained by using a local averaging operator. Numerical experiments are given to confirm the reliability and efficiency of the error estimators. Copyright © 2020, The Authors. All rights reserved.

关键词： Finite element method

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Electron Acceptor Molecule Doping Induced π–π Interaction to Promote Charge Transport Kinetics for Efficient and Stable 2D/3D Perovskite Solar Cells

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Angewandte Chemie 2023年第26期135卷

作者： Lian Wang Bohong Chang Hui Li Yutong Wu Luyuan Zhang Longwei Yin Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials Ministry of Education School of Materials Science and Engineering Shandong University 250061 Jinan P. R.China Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) Contribution: Data curation (supporting) Investigation (supporting) Methodology (supporting) Writing - original draft (supporting) Contribution: Formal analysis (supporting) Investigation (supporting) Methodology (supporting) Writing - original draft (supporting) Contribution: Conceptualization (supporting) Methodology (supporting) Writing - original draft (supporting) Contribution: Formal analysis (supporting) Investigation (supporting) Writing - original draft (supporting) Contribution: Conceptualization (lead) Data curation (lead) Funding acquisition (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead)

Although the incorporation of 2D perovskite into 3D perovskite can greatly enhance intrinsic stability, power conversion efficiency (PCE) of 2D/3D perovskite is still inferior to its 3D counterpart due to poor carrier transport kinetics resulted from the quantum and dielectric confinement of 2D component. To overcome this issue, the electron acceptor molecule 1,2,4,5-tetracyanobenzene (TCNB) was introduced to trigger intermolecular π–π interaction in 2D perovskite along with the electronic doping of 2D/3D perovskite to improve charge transfer efficiency. By virtue of high electron affinity, TCNB can undergo electron transfer reaction and subsequently establish π–π interaction with 1-naphthalenemethylammonium (NMA) cations, greatly strengthening lattice rigidity and reducing exciton binding energy. Transmission electron microscopy results demonstrate that 2D phases are mainly distributed at grain boundaries, reducing defect density and weakening nonradiative recombination. Meanwhile, the p-type doping of perovskite by TCNB optimizes energy level alignment at perovskite/hole transport layer interface. Consequently, PCE of champion device is significantly boosted to 24.01 %. The unencapsulated device retains an initial efficiency close to 94 % after exposure to ambient environment for over 1000 h. This work paves a novel path for designing new mixed-dimensional perovskite solar cells with high PCE and superior stability.

关键词： 2D/3D Perovskite Charge Transport Electron Acceptor Molecule Solar Cells π–π Interaction

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