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Nonlinear Optics, NLO 2017

作者： Shang, Jianming Lu, Xiaoli Jiang, Tianwei Lu, Yueming Yu, Song Gu, Wanyi State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

A simple mode-locked method is presented based on nonlinear polarization evolution with only a polarization beam splitter (PBS) in free space, which have potential for high repetition. © OSA 2017.

ISBN: (纸本)9780960038060

A simple mode-locked method is presented based on nonlinear polarization evolution with only a polarization beam splitter (PBS) in free space, which have potential for high repetition. © OSA 2017.

关键词： Polarization

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DropConnect Regularization Method with Sparsity Constraint for Neural Networks

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Chinese Journal of Electronics 2016年第1期25卷 152-158页

作者： LIAN Zifeng JING Xiaojun WANG Xiaohan HUANG Hai TAN Youheng CUI Yuanhao Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications School of Software and Microelectronics Peking University

Drop Connect is a recently introduced algorithm to prevent the co-adaptation of feature *** to Dropout, Drop Connect gains state-of-the-art results on several image recognition benchmarks. Motivated by the success of Drop Connect, we extended this algorithm with the ability of sparse feature selection. In Drop Connect algorithm, the dropping masks of weights are generated using Bernoulli gating variables that are independent of the weights and activations. We introduce a new strategy to generate masks depending on the outputs of previous layer. Using this method, neurons which are promising to produce sparser features will be assigned a bigger possibility to keep active in the forward and backward propagations. We then evaluate such sparsity constrained Drop Connect on MNIST and CIFAR datasets in comparison with ordinary Drop Connect and Dropout method. The results show that our new method improves the sparsity of features significantly, while not degrading the precision.

关键词： Drop Connect Sparse regularization Deep learning Neural networks

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Understanding information interactions in diffusion： an evolutionary game-theoretic perspective

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Frontiers of Computer Science 2016年第3期10卷 518-531页

作者： Yuan SU Xi ZHANG Lixin LIU Shouyou SONG Binxing FANG Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationSchool of Computer Science Beijing University of Posts and Telecommunications Beijing 100876 China

Social networks are fundamental media for dif- fusion of information and contagions appear at some node of the network and get propagated over the edges. Prior re- searches mainly focus on each contagion spreading indepen- dently, regardless of multiple contagions＇ interactions as they propagate at the same time. In the real world, simultaneous news and events usually have to compete for user＇s attention to get propagated. In some other cases, they can cooperate with each other and achieve more influences. In this paper, an evolutionary game theoretic framework is proposed to model the interactions among multiple con- tagions. The basic idea is that different contagions in social networks are similar to the multiple organisms in a popula- tion, and the diffusion process is as organisms interact and then evolve from one state to another. This framework statis- tically learns the payoffs as contagions interacting with each other and builds the payoff matrix. Since learning payoffs for all pairs of contagions IS almost impossible （quadratic in the number of contagions）, a contagion clustering method is proposed in order to decrease the number of parameters to fit, which makes our approach efficient and scalable. To ver- ify the proposed framework, we conduct experiments by us- ing real-world information spreading dataset of Digg. Exper- imental results show that the proposed game theoretic frame- work helps to comprehend the information diffusion process better and can predict users＇ forwarding behaviors with more accuracy than the previous studies. The analyses of evolution dynamics of contagions and evolutionarily stable strategy re- veal whether a contagion can be promoted or suppressed by others in the diffusion process.

关键词： social networks information diffusion gametheory evolutionary game evolution dynamics

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A recommendation approach based on latent factors prediction of recurrent neural network 12th

A recommendation approach based on latent factors prediction...

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12th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2017

作者： Li, Ruihong Zuo, Xingquan Wang, Pan Zhao, Xinchao School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811071782

Recommender systems have received much attention due to their wide applications. Current recommender approaches typically recommend items to user based on the rating prediction. However, the predicted ratings cannot truly reflect users interests on items because the rating prediction is usually based on history data and does not consider the effect of time factor on uses interests (behaviors). In this paper, we propose a recommendation approach combining the matrix factorization and a recurrent neural network. In this approach, all the items rated by a user are considered as time series data. The matrix factorization is used to obtain latent vectors of those items. The recurrent neural network is taken as a time series prediction model and trained by the latent vectors of historical items, and then the trained model is used to predict the latent vector of the item to be recommended. Finally, a recommendation list is formed by mapping the latent vector into a set of items. Experimental results show that the proposed approach is able to produce an effective recommend list and outperforms those comparative approaches. © Springer Nature Singapore Pte Ltd 2017.

关键词： Recommender systems

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Enhancing stock market prediction with extended coupled Hidden Markov Model over multi-sourced data

arXiv

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arXiv 2018年

作者： Zhang, Xi Li, Yixuan Wang, Senzhang Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Institute of Electronic and Information Engineering UESTC Guangdong China Department of Computer Science University of Illinois ChicagoIL United States Institute for Data Science Tsinghua University Beijing China

Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market uctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an Extended Coupled Hidden Markov Model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the uctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Hidden Markov models

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Energy Efficiency and Spectral-Efficiency Tradeoff in Downlink NOMA Systems

Energy Efficiency and Spectral-Efficiency Tradeoff in Downli...

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International Conference on Communications Workshops

作者： Qian Liu Fangqing Tan Tiejun Lv Hui Gao Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications China 100876

ISBN: (纸本)9781509015269

Non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology for the fifth generation (5G) mobile communication due to its high spectral efficiency (SE). Green radio (GR) which focuses on energy efficiency (EE) is becoming an inevitable trend. In this paper, we study the tradeoff between EE and SE in downlink NOMA systems. The tradeoff problem is formulated as an optimization problem in which the basic idea is to maximize EE under a satisfying minimum SE requirement. It is proved that the system EE is a strictly quasi-concave function of transmission power. By formulating the optimization problem as a bilayer optimization problem, the optimal solution to the optimization problem is obtained, including optimal power allocation coefficients among multiplexed users and optimal transmission power value. Simulation results are provided to further confirm our theoretical findings and the effectiveness of our proposed power allocation scheme for achieving a flexible tradeoff between EE and SE.

关键词： Downlink NOMA Systems

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Improving stock market prediction via heterogeneous information fusion

arXiv

引用

arXiv 2018年

作者： Zhang, Xi Zhang, Yunjia Wang, Senzhang Yao, Yuntao Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Computer Science University of Illinois at ChicagoIL60607 United States Institute for Data Science Tsinghua University Beijing100084 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan Guangdong523808 China

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people’s sentiments towards the market and stocks, have been proved to play important roles in the stocks’ volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users’ sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsicrelations among the events and the investors’ sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in HBase

Implementing an Inference Engine for RDFS/OWL Constructs and...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Zhengbo Liu Wenbin Yao Dongbin Wang National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ministry of Education for Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

The Inference Engine is an integral part of Semantic Data Stores. In this paper, we describe our experience of implementing a scalable inference engine for HBase Semantic Data Store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, and ii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of the HBase Database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using LUBM semantic datasets demonstrates the effectiveness of our approach.

关键词： Engines Ontologies Semantics Cognition Resource description framework OWL Databases

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A Recommendation System Based on Multi-attribute 9

A Recommendation System Based on Multi-attribute

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9th International Conference on service Science, ICSS 2016

作者： Dong, Jiali Li, Xiaoyong Fang, Binxing Beijing University of Post and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9781509027279

In recent years, more and more internet companies trend to fuse their products with personal recommendation functions. Although the idea of this business model satisfies characteristic of Internet industry felicitously, scientists and engineers gradually find out that the effect is worse than they expected. The quality of recommendation is limited by cold start, data sparse, lack of learning contextual aware, and so on. One of the most important drawbacks of existing recommendation systems is that they usually use only rating matrix as useful information and not fully consider item attributes. In this paper, we proposed an optimized recommendation system, which based on modeling of items in a multidimensional space of item's attribute. The presented approach takes item attributes, user preferences and user context into consideration, and is mainly composed of two modules: (1) attribute-latent factor model, which is used to show user latent preference for each attribute, (2) multi-attribute model, which is used to show user preference between all attributes. The research is conducted in a practical application environment, and the preliminary experimental results show the validity of the recommendation. © 2016 IEEE.

关键词： Recommender systems

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Fog computing for vehicular Ad-hoc networks: paradigms, scenarios, and issues

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The Journal of China Universities of Posts and Telecommunications 2016年第2期23卷 56-65,96页

作者： Kang Kai Wang Cong Luo Tao School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts Telecommunications

Vehicular Ad-hoc networks（VANETs） are kinds of mobile Ad-hoc networks（MANETs）, which consist of mobile vehicles with on-board units（OBUs） and roadside units（RSUs）. With the rapid development of computation and communication technologies, peripheral or incremental changes in VANETs evolve into a revolution in process. Cloud computing as a solution has been deployed to satisfy vehicles in VANETs which are expected to require resources（such as computing, storage and networking）. Recently, with special requirements of mobility, location awareness, and low latency, there has been growing interest in research into the role of fog computing in VANETs. The merging of fog computing with VANETs opens an area of possibilities for applications and services on the edge of the cloud computing. Fog computing deploys highly virtualized computing and communication facilities at the proximity of mobile vehicles in VANET. Mobile vehicles in VANET can also demand services of low-latency and short-distance local connections via fog computing. This paper presents the current state of the research and future perspectives of fog computing in VANETs. Moreover, we discuss the characteristics of fog computing and services based on fog computing platform provided for VANETs. In this paper, some opportunities for challenges and issues are mentioned, related techniques that need to be considered have been discussed in the context of fog computing in VANETs. Finally, we discuss about research directions of potential future work for fog computing in VANETs. Within this article, readers can have a more thorough understanding of fog computing for VANETs and the trends in this domain.

关键词： VANET fog computing cloud computing application vehicular cloud computing

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