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arXiv 2018年

作者： Huang, Jieyun Zhang, Yunjia Zhang, Jialai Zhang, Xi Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Electronic Information Sichuan University Chengdu China

The investment on the stock market is prone to be affected by the Internet. For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model that not only considers the stock correlations but also supports multi-source data fusion. Our proposed model first utilizes tensor to integrate the multi-sourced data, including financial Web news, investors' sentiments extracted from the social network and some quantitative data on stocks. In this way, the intrinsic relationships among different information sources can be captured, and meanwhile, multi-sourced information can be complemented to solve the data sparsity problem. Secondly, we propose an improved sub-mode coordinate algorithm (SMC). SMC is based on the stock similarity, aiming to reduce the variance of their subspace in each dimension produced by the tensor decomposition. The algorithm is able to improve the quality of the input features, and thus improves the prediction accuracy. And the paper utilizes the Long Short-Term Memory (LSTM) neural network model to predict the stock fluctuation trends. Finally, the experiments on 78 A-share stocks in CSI 100 and thirteen popular HK stocks in the year 2015 and 2016 are conducted. The results demonstrate the improvement on the prediction accuracy and the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Comparison of three data mining methods in predicting 5-Year Survival of Colorectal Cancer Patients

Comparison of three data mining methods in predicting 5-Year...

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智能时代的创新、融合与发展——2018全国博士后学术交流会

作者： LUO Yan SUN Ya-wei FU Qun-chao XUE Teng-fei ZHOU Ping School of Software Engineering Beijing University of Posts and Telecommunications Beijing 100876 China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education Beijing 100876 China

The prediction of colorectal cancer (CRC) survivability has always been a challenging research *** the importance of predicting CRC Patient's survival rates,this article compare the performance of three data mining methods: decision trees(DTs),artificial neural networks(ANNs) and support vector machines(SVMs),for predicting 5-year survival of CRC patients to assist clinicians in making treatment *** CRC dataset used to build the prediction model comes from the surveillance,epidemiology,and end results(SEER) *** 5-fold cross-validation and random forest algorithm were respectively utilized for measuring the model predictive accuracy and the importance of *** results show that the predictive accuracy of ANNs (0.73) and SVMs (0.75) were higher than that of decision trees,and they also have the best result in the area under the ROC curve (AUC =0.82).This result may indicate high predictive power of ANNs and SVMs for predicting 5-year survival of CRC patients.

关键词： data mining 5-Year Survival CRC SEER

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An Attack Signatures Generation Sequence Alignment Algorithm Based on Production Rules

An Attack Signatures Generation Sequence Alignment Algorithm...

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International Conference on Communication Software and Networks, ICCSN

作者： Muhan Xue Wen Yu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

Aiming at the problem that the result of some attack sequence alignment methods is not necessarily the optimal expression of their characteristics. This paper presents a Production Rule Sequence Alignment Algorithm (PRSA) combining the production rule inference mechanism which improves traditional sequence alignment algorithm. A new accumulation of knowledge is obtained by changing the way of sequence alignment and the transformation of signatures. PRSA overcomes the problem that the extraction results produced by the traditional sequence alignment algorithm cannot express the attack signature accurately. Then, we establish an automatic attack signature generation model based on PRSA. The experimental results show that the matching results obtained by using PRSA can express the signatures of the attack accurately and improve the detection rate of the attacks.

关键词： Production Feature extraction Inference algorithms Classification algorithms Software algorithms Libraries Data models

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Maximizing Influence Propagation in Networks by User-Relational Iterative Ranking Algorithm

Maximizing Influence Propagation in Networks by User-Relatio...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Wenbin Yao Lijuan Zhang Xiaoyong Li Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia 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

ISBN: (纸本)9781728111421;9781728111414

The issue of maximizing the influence is a hot topic in the research of social network. Many researchers have studied from the perspective of the structure of the network such as the LeaderRank algorithm. However, the algorithm lacks semantic interpretability and explanations for user behavior. Therefore, we propose a novel URI (user-relational iterative) rank to address the above issues. The URI rank is divided into two parts to obtain the values of user influence. The first part is the forwarding probability based on the user relationship. We introduce the relationship between users to the user transition probability and use the random forest to quantify the value of the forwarding probability. The second part is the random transition probability based on the ground node. We optimize the weight assigning of the random transition probability by combining static decentralization and dynamic decentralization. Thus, we more accurately represent the user's random transfer behavior. The experiments performed on the Sina Micro-Blog Dataset show that our algorithm outperforms the existing algorithms.

关键词： Social networking (online) Telecommunications Greedy algorithms Complex networks Software Multimedia communication Multimedia computing

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Faster-than-Nyquist Signaling for Optical Communications

Faster-than-Nyquist Signaling for Optical Communications

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OptoElectronics and Communications Conference, OECC

作者： Yaojun Qiao Ji Zhou Mengqi Guo Xizi Tang Jia Qi Yueming Lu School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

In this paper, we will discuss the faster-than-Nyquist (FTN) signaling for optical communications, including the single-carrier and multi-carrier FTN signaling.

ISBN: (纸本)9781538691465

In this paper, we will discuss the faster-than-Nyquist (FTN) signaling for optical communications, including the single-carrier and multi-carrier FTN signaling.

关键词： Bandwidth OFDM MIMO communication Optical fiber communication Optical filters Optical transmitters

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Neural Network Based Target User Recognition Model for Network Community

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IOP Conference Series: Earth and Environmental Science 2019年第1期234卷

作者： Xu Wu Yulun Dai Xiaqing Xie Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Library Beijing 100876 China

For multi-dimensional feature of user data in network community, available methods mainly use Rank scoring algorithm or user classification algorithm for target user recognition. However, Rank method has a low performance, and the classification algorithm needs high construction cost. Therefore, this paper uses a target user recognition model integrating the user content analysis and the user behavior analysis to improve performance and speed of the target user recognition by a neural network content analysis model with a single-layer neural network and N-gram features for discovering automatically the user feature. The proposed method outperforms the current Rank scoring and classification methods in terms of performance, in which the F value reaches 0.89 and the accuracy reaches 0.91. Moreover, avoiding the cost of manual design dependent on specific tasks shortens the training time. Ten thousand data can be modeled in one minute.

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Exploiting investors social network for stock prediction in China's market

arXiv

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arXiv 2018年

作者： Zhang, Xi Shi, Jiawei Wang, Di Fang, Binxing Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China Institute of Electronic and Information Engineering UESTC in Guangdong Dongguan Guangdong523808 China

Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail investors are prone to be influenced by news or other social media, psychological and behavioral features extracted from social media platforms are thought to well predict stock price movements in the China's market. Recent advances in the investor social network in China enables the extraction of such features from web-scale data. In this paper, on the basis of tweets from Xueqiu, a popular Chinese Twitter-like social platform specialized for investors, we analyze features with regard to collective sentiment and perception on stock relatedness and predict stock price movements by employing nonlinear models. The features of interest prove to be effective in our experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Sentiment analysis

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An efficient nonlinear equalizer for 40-Gb/s PAM4-PON systems

An efficient nonlinear equalizer for 40-Gb/s PAM4-PON system...

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Optical Fiber Communication Conference, OFC 2018

作者： Tang, Xizi Zhou, Ji Guo, Mengqi Qi, Jia Zhang, Tiantian Zhang, Zhenshan Lu, Yueming Qiao, Yaojun State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

We firstly propose a decision feedback equalizer (DFE) based on a novel nonlinear filter structure for 40-Gb/s PAM4-PON systems. The proposed equalizer achieves similar performance with Volterra DFE at only 21.7% tap ... 详细信息

ISBN: (纸本)9781943580385

关键词： Decision feedback equalizers

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A Simple mode-locked method using a polarization beam splitter

A Simple mode-locked method using a polarization beam splitt...

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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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