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作者： Yang, Bin Mao, Guoqiang Ding, Ming Ge, Xiaohu Tao, Xiaofeng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China School of Computing and Communication University of Technology Sydney UltimoNSW2007 Australia Data61 CSIRO CanberraACT2601 Australia National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing100876 China

Considering both non-line-of-sight (NLoS) and line-of-sight (LoS) transmissions, the transitional behaviors from noise-limited regime to dense interference-limited regime have been investigated for the fifth generation (5G) small cell networks (SCNs). Besides, we identify four performance regimes based on base station (BS) density, i.e., 1) the noise-limited regime, 2) the signal-dominated regime, 3) the interference-dominated regime, and 4) the interference-limited regime. To characterize the performance regime, we propose a unified framework analyzing the future 5G wireless networks over generalized shadowing/fading channels, in which the user association schemes based on the strongest instantaneous received power and the strongest average received power can be studied, while NLoS/LoS transmissions and multislop path loss model are considered. Simulation results indicate that different factors, i.e., noise, desired signal, and interference, successively and separately dominate the network performance with the increase of BS density. Hence, our results shed new light on the design and management of SCNs in urban and rural areas with different BS deployment densities. © 1967-2012 IEEE.

关键词： Wireless networks

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational Data and Social networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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Efficient attribute-based encryption with blackbox traceability 12th

Efficient attribute-based encryption with blackbox traceabil...

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12th International Conference on Provable security, ProvSec 2018

作者： Xu, Shengmin Yang, Guomin Mu, Yi Liu, Ximeng Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Wollongong Australia School of Information Systems Singapore Management University Singapore Singapore College of Mathematics and Computer Science Fuzhou University FuzhouFujian China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China

ISBN: (纸本)9783030014452

Traitor tracing scheme can be used to identify a decryption key is illegally used in public-key encryption. In CCS’13, Liu et al. proposed an attribute-based traitor tracing (ABTT) scheme with blackbox traceability which can trace decryption keys embedded in a decryption blackbox/device rather than tracing a well-formed decryption key. However, the existing ABTT schemes with blackbox traceability are based on composite order group and the size of the decryption key depends on the policies and the number of system users. In this paper, we revisit blackbox ABTT and introduce a new primitive called attribute-based set encryption (ABSE) based on key-policy ABE (KP-ABE) and identity-based set encryption (IBSE), which allows aggregation of multiple related policies and reduce the decryption key size in ABTT to be irrelevant to the number of system users. We present a generic construction of the ABTT scheme from our proposed ABSE scheme and fingerprint code based on the Boneh-Naor paradigm in CCS’08. We then give a concrete construction of the ABSE scheme which can be proven secure in the random oracle model under the decisional BDH assumption and a variant of q-BDHE assumption. © Springer Nature Switzerland AG 2018.

关键词： Public key cryptography

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A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM networks

A Multi-label Classifier for Human Protein Subcellular Local...

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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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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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An Improved Convolutional Neural network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc networks

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Journal of Physics: Conference Series 2019年第1期1288卷

作者： Zhixiao Wang Mingyu Chen Wenyao Yan Wendong Wang Ang Gao Gaoyang Nie Feng Wang Shaobo Yang Xi'an University of Technology Xi'an 710048 China Goettingen University Goettingen 37077 Germany Xi'an Jiaotong University Xi'an 710049 China Shaanxi Provincial Key Laboratory of Network Computing and Security Technology Xi'an 710048 China College of Xi'an Innovation Yan'an University Xi'an 710100 China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

Ad-Hoc network which is one kind of self-organized networks is much more vulnerable than the infrastructural network with the properties of highly changeable linkage, dynamic structure, and wireless connections so that the tradition intrusion detection system (IDS) should be improved to adapt in such network with limited computing resources and open channels. To ensure the security in Ad-Hoc network, the efficient anomaly detection methods should be probed. Over the past years, many studies have implemented anomaly detection methods (intrusion detection techniques) based on machine-learning methods in this field. This article analyzes the existing security problem in Ad-Hoc network, presents the basic theory of intrusion detection for Ad-Hoc network, and reviews the current and recent anomaly detection methods used machine learning techniques in the intrusion detection system.

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Quantum private comparison based on quantum dense coding

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Science China(Information Sciences) 2016年第11期59卷 195-207页

作者： Feng WANG Mingxing LUO Huiran LI Zhiguo QU Xiaojun WANG College of Mathematical Sciences Dezhou University Information Security and National Computing Grid Laboratory Southwest Jiaotong University Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology School of Electronic Engineering Dublin City University

A serious problem in cloud computing is privacy information protection. This study proposes a new private comparison protocol using Einstein-Podolsky-Rosen(EPR) pairs. This protocol allows two parties to secretly compare their classical information. Quantum dense coding enables the comparison task to be completed with the help of a classical semi-honest center. A one-step transmission scheme and designed decoy photons can be used against various quantum attacks. The new protocol can ensure fairness, efficiency, and security. The classical semi-honest center cannot learn any information about the private inputs of the players. Moreover, this scheme can be easily generalized using the general EPR pairs in order to improve the transmission efficiency.

关键词： private comparison multiparty secure computation classical semi-honesty center quantum dense coding general EPR pair

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Improved quantum ripple-carry addition circuit

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Science China(Information Sciences) 2016年第4期59卷 104-111页

作者： Feng WANG Mingxing LUO Huiran LI Zhiguo QU Xiaojun WANG College of mathematical sciences Dezhou University Information Security and National Computing Grid Laboratory Southwest Jiaotong University Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology School of Electronic Engineering Dublin City University

A serious obstacle to large-scale quantum algorithms is the large number of elementary gates, such as the controlled-NOT gate or Toffoli gate. Herein, we present an improved linear-depth ripple-carry quantum addition circuit, which is an elementary circuit used for quantum computations. Compared with previous addition circuits costing at least two Toffoli gates for each bit of output, the proposed adder uses only a single Toffoli gate. Moreover, our circuit may be used to construct reversible circuits for modular multiplication, Cx mod M with x < M, arising as components of Shor’s algorithm. Our modular-multiplication circuits are simpler than previous constructions, and may be used as primitive circuits for quantum computations.

关键词： quantum addition circuit modular multiplication CNOT gate Toffoli gate circuit complexity

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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

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Journal of Physics: Conference Series 2019年第1期1284卷

作者： Zhixiao Wang Wenyao Yan Wendong Wang Ang Gao Lei Yu Shaobo Yang GaoYang Nie School of Computer Science and Engineering Xi'an University of Technology 710048 Xi'an China Telematics Group the University of Goettingen 37077 Goettingen Germany Shaanxi Key Laboratory of Network Computing and Security 710048 Xi'an China School of Electronic and Information Engineering Xi'an Jiaotong University 710048 Xi'an China College of Xi'an Innovation Yan'an University 710100 Xi'an China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

Based on the huge volumes of user check-in data in LBSNs, users' intrinsic mobility patterns can be well explored, which is fundamental for predicting where a user will visit next given his/her historical check-in records. As there are various types of nodes and interactions in LBSNs, they can be treated as Heterogeneous Information network (HIN) where multiple semantic meta-paths can be extracted. Inspired by the recent success of meta-path context based embedding techniques in HIN, in this paper, we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location prediction. Experimental results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.

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