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2018 International Conference on security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Li, Tianhao Sun, Fengyang Sun, Runyuan Wang, Lin Li, Meihui Yang, Huawei University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Shandong University Jinan Shandong Provincial Key Laboratory of Oral Tissue Regeneration Department of Human Microbiome Jinan Shandong250012 China Jinan Kingmon Information Technology Co. Ltd. Jinan250022 China

ISBN: (纸本)9781728105512

Correct use of Chinese herbal medicines is vital to life safety of the patients. Chinese herbal medicine classification is very important for the correct use of Chinese herbal medicines. Traditional methods like microfeature identification and physiochemical identification are inefficient due to the various kinds and different conditions of Chinese herbal medicines. Therefore, we adopt a multiscale convolutional neural network (CNN) model with data augmentation technology to classify Chinese herbal medicines. The data augmentation techniques solve the problem of less data on Chinese herbal medicines. Multiscale technology extracts more useful features for Chinese herbal medicine classification. The experiments show favorable accuracy. © 2018 IEEE.

关键词： Image classification

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Load Feedback-Based Resource Scheduling and Dynamic Migration-Based Data Locality for Virtual Hadoop Clusters in OpenStack-Based Clouds

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Tsinghua Science and technology 2017年第2期22卷 149-159页

作者： Dan Tao Zhaowen Lin Bingxu Wang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003China Network and Information Center Institute of Network TechnologyScience and Technology on Information Transmission and Dissemination in Communication Networks LaboratoryNational Engineering Laboratory for Mobile Network SecurityBeijing University of Posts and TelecommunicationsBeijing 100876China

With cloud computing technology becoming more mature, it is essential to combine the big data processing tool Hadoop with the Infrastructure as a Service（Iaa S） cloud platform. In this study, we first propose a new Dynamic Hadoop Cluster on Iaa S（DHCI） architecture, which includes four key modules： monitoring,scheduling, Virtual Machine（VM） management, and VM migration modules. The load of both physical hosts and VMs is collected by the monitoring module and can be used to design resource scheduling and data locality solutions. Second, we present a simple load feedback-based resource scheduling scheme. The resource allocation can be avoided on overburdened physical hosts or the strong scalability of virtual cluster can be achieved by fluctuating the number of VMs. To improve the flexibility, we adopt the separated deployment of the computation and storage VMs in the DHCI architecture, which negatively impacts the data locality. Third, we reuse the method of VM migration and propose a dynamic migration-based data locality scheme using parallel computing entropy. We migrate the computation nodes to different host（s） or rack（s） where the corresponding storage nodes are deployed to satisfy the requirement of data locality. We evaluate our solutions in a realistic scenario based on Open *** experimental results demonstrate the effectiveness of our solutions that contribute to balance the workload and performance improvement, even under heavy-loaded cloud system conditions.

关键词： Hadoop resource scheduling data locality Infrastructure as a Service（Iaas） OpenStack

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Research on System Dynamics and Agent Hybrid Modeling Method for Multi-Energy Collaborative Control Under Ubiquitous Power Internet

Research on System Dynamics and Agent Hybrid Modeling Method...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Fuxing Zhang Yingjie Zhang Xiong Zhan Juan Luo Honglei Miao Yonghua Gui College of Computer Science and Electronic Engineering Hunan University Changsha China State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute Beijing China Hardware R&D Department HNAC Technology Co. Ltd. Changsha China

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

In order to promote the study of system dynamics (SD) and multi-agent theory and its wide application in the field of renewable energy, a hybrid modeling method of SD and Agent is proposed in this paper, which is suitable for the multi-energy collaborative management and control of regional cooling, heating and power under the background of the ubiquitous Internet of Things. Based on the detailed analysis of the lack of single modeling paradigm of system dynamics and the necessity and feasibility of mixed modeling of SD and Agent, the basic types of hybrid modeling of SD and Agent for regional multi-energy cooperative management and control are defined, and the application forms, simulation platforms, problems and ideas of hybrid modeling of SD and Agent in regional multi-energy cooperative management are discussed and analyzed, which can deepen the innovative application of SD and Agent hybrid modeling in multiple fields and assist relevant managers in scientific decision-making.

关键词： Mathematical model Analytical models Adaptation models System dynamics Biological system modeling Hybrid power systems Cooling

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Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach

Accurate Recovery of Internet Traffic Data: A Sequential Ten...

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作者： Xie, Kun Wang, Lele Wang, Xin Xie, Gaogang Wen, Jigang Zhang, Guangxing Cao, Jiannong Zhang, Dafang College of Computer Science and Electronics Engineering Hunan University Changsha410006 China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony BrookNY11794 United States Network Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as capacity planning and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a 2-D matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel sequential tensor completion algorithm, which can efficiently exploit the tensor decomposition result based on the previous traffic data to derive the tensor decomposition upon arriving of new data. Furthermore, to better capture the changes of data correlation over time, we propose a dynamic sequential tensor completion algorithm. To the best of our knowledge, we are the first to propose sequential tensor completion algorithms to significantly speed up the traffic data recovery process. This facilitates the modeling of Internet traffic with the tensor to well exploit the hidden structures of traffic data for more accurate missing data inference. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithms can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high. © 1993-2012 IEEE.

关键词： Tensors

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Time-varying massive MIMO channel estimation: Capturing, reconstruction and restoration

arXiv

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arXiv 2019年

作者： Li, Muye Zhang, Shun Zhao, Nan Zhang, Weile Wang, Xianbin State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Information and Communication Engineering Dalian University of Technology Dalian116024 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Engineering Western University LondonON Canada

On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-grid bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： MIMO systems

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Correction to: Underwater image enhancement using an edge-preserving filtering Retinex algorithm

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Multimedia Tools and Applications 2020年第25期79卷 17279-17279页

作者： Peixian Zhuang Xinghao Ding Jiangsu Key Laboratory of Meteorological Observation and Information Processing Jiangsu Technology and Engineering Center of Meteorological Sensor Network School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen China

In the original publication, Equations were incorrectly presented. The original article has been corrected.

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A Study on Influential User Identification in Online Social networks

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Chinese Journal of Electronics 2016年第3期25卷 467-473页

作者： WANG Nan SUN Qindong ZHOU Yadong SHEN Si Shaanxi Key Laboratory of Network Computing and Security Xi'an University of Technology Ministry of Education Key Laboratory For Intelligent Network and Network Security Xi'an Jiaotong University

Influential user evaluation is great importance in many application areas of online social *** order to identify influential users in a more adequate and practical way, we propose a Dynamic regional interaction model(DRI) to evaluate user influence in online social networks. Influential users can be identified by the influence effect on different distance users based on dynamic regional interaction model. We have applied the influential user identification method to Sina Weibo and the experimental results show that compared with the existing methods the proposed method can identify the influence users in a more accuracy and efficiency way.

关键词： Online social networks Influential user identification Dynamic regional interaction model(DRI) User influence evaluation

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MFPR: A Personalized Ranking Recommendation with Multiple Feedback

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ACM Transactions on Social computing 2018年第2期1卷 1–22页

作者： Shi, Chuan Liu, Jian Zhang, Yiding Hu, Binbin Liu, Shenghua Yu, Philip S. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications No. 10 Xi Tu Cheng Road Beijing China Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing China Department of Computer Science University of Illinois at Chicago 851 South Morgan Street Chicago United States

Recently, recommender systems have played an important role in improving web user experiences and increasing profits. Recommender systems exploit users' behavioral history (i.e., feedback on items) to build models. The feedback usually includes explicit feedback (e.g., ratings) and implicit feedback (e.g., browsing history, click logs), which are both useful for improving recommendations. However, as far as we are concerned, no existing works have integrated both explicit and multiple implicit feedback simultaneously. Therefore, we propose a unified and flexible model, named Multiple Feedback-based Personalized Ranking (MFPR), to make full use of multiple feedback, which uses a personalized ranking framework. To train model MFPR, we design an algorithm to generate ordered item pairs as labeled data, with consideration of both rating scores and multiple implicit feedback. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model. With the integration of multiple feedback, MFPR significantly improves recommendation performance. © 2018 ACM.

关键词： Recommender systems

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Coherence-based automated essay scoring using self-attention 17th

Coherence-based automated essay scoring using self-attention

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17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Li, Xia Chen, Minping Nie, Jianyun Liu, Zhenxing Feng, Ziheng Cai, Yingdan Key Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China School of Information Science and Technology/School of Cyber Security Guangdong University of Foreign Studies Guangzhou China Department of Computer Science and Operations Research University of Montreal Montreal Canada

ISBN: (纸本)9783030017156

Automated essay scoring aims to score an essay automatically without any human assistance. Traditional methods heavily rely on manual feature engineering, making it expensive to extract the features. Some recent studies used neural-network-based scoring models to avoid feature engineering. Most of them used CNN or RNN to learn the representation of the essay. Although these models can cope with relationships between words within a short distance, they are limited in capturing long-distance relationships across sentences. In particular, it is difficult to assess the coherence of the essay, which is an essential criterion in essay scoring. In this paper, we use self-attention to capture useful long-distance relationships between words so as to estimate a coherence score. We tested our model on two datasets (ASAP and a new non-native speaker dataset). In both cases, our model outperforms the existing state-of-the-art models. © Springer Nature Switzerland AG 2018.

关键词： Automation

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AClog: Attack Chain Construction Based on Log Correlation

AClog: Attack Chain Construction Based on Log Correlation

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IEEE Global Communications Conference

作者： Teng Li Jianfeng Ma Qingqi Pei Yulong Shen Chi Lin Siqi Ma Mohammad S. Obaidat School of Cyber Engineering Xidian University Shaanxi China Shaanxi Key Laboratory of BlockChain and Security Computing Xidian University Shaanxi China School of Computer Science Xidian University Shaanxi China School of Software Technology Dalian University of Technology Dalian China Commonwealth Scientific and Industrial Research Organisation Sydney Australia College of Computing and Informatics University of Sharjah UAE University of Jordan Jordan Nazarbayev University Kazakhstan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Before the final attack happens, clandestine attackers conduct sequenced stages for being stealthy and elusive. These attacks can leave clues in several different log files. However existing approaches can only detect the anomalies using single type of log and fail to reveal all of the attack steps through log integration and correlation. Such methods can hardly detect the relationships among events and prevent the attack in advance. Additionally, traditional machine learning or data mining in log analysis has a high overhead in computing which is impractically applied in a real product or system. To address these problems, we present AClog, a multiple log correlated analysis system to construct the attack chain. Inspired by penetration testing and social network analysis, we transfer the attack provenance as an event relationship discover problem. We use different logs to form the steps of the system and regard them as the event sequences before the attack. Then, we leverage Fast Linear SVM and Longest Common Subsequences to find out the regular steps before the attack. Finally, we spot the corresponding log sequences to identify the pre-attack steps proactively. We apply our approach in the attack prediction of a cloud computing platform and a university network. The results show that the proposed method can effectively and precisely construct the attack steps and identify the corresponding syslogs.

关键词： Correlation Clustering algorithms Support vector machines Windows

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