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Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

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SWIPT-Aided Secure Beamforming Design for Downlink Cooperative NOMA Systems 6

SWIPT-Aided Secure Beamforming Design for Downlink Cooperati...

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6th Global Wireless Summit, GWS 2018

作者： Hao, Yunqin Lv, Tiejun Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9781538642887

In this paper, we consider a downlink non-orthogonal multiple access system, in which base station emits secret messages to two legitimate users in the presence of multiple eavesdroppers, and all the eavesdroppers collude to form joint receive beamforming, in an attempt to enhance their interceptions. In light of this, a cooperative simultaneous wireless information and power transfer non-orthogonal multiple access secure transmission protocol is proposed, where the legitimate user with a stronger channel condition acts as an energy harvesting relay to help that with a weaker channel condition. Following this, a secrecy sum rate maximization problem is formulated, under the constraints of transmit power and quality of service requirements. By taking both the single-antenna and multiple-antenna scenarios into account, two different optimization schemes, i.e., the power control scheme, and the null-space beamforming design scheme, are presented to maximize the secrecy sum rate. Numerical results verify the superiority of our proposed schemes over the benchmarks. © 2018 IEEE.

关键词： Beamforming

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Ranking Modules for Integrate Testing Based on PageRank Algorithm

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Chinese Journal of Electronics 2017年第5期26卷 993-998页

作者： SUN Qi Krishna Ricky MONIZ YUAN Yuyu Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing University of Posts and Telecommunications

The testing industry need to prioritize the limited resources and focus on testing modules whose failure is mostly likely to cause *** paper discusses a method that can rank modules in a software package for integrate testing using the PageR ank *** this algorithm,a sequences of random walks iteratively can find a high likelihood of encountering a node,which is interpreted as it being an important performance *** experiment result prove that the proposed method actually can be used to prioritize testing of specific modules when testing resource are scarce.

关键词： Software testing Integrate testing PageRank algorithm Testing prioritize

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A tensor-based sub-mode coordinate algorithm for stock prediction 3

A tensor-based sub-mode coordinate algorithm for stock predi...

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3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

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

ISBN: (纸本)9781538642108

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. © 2018 IEEE.

关键词： Tensors

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Barebones particle swarm optimization with a neighborhood search strategy for feature selection 1

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13th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2018

作者： Qiu, Chenye Zuo, Xingquan School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

Feature selection is a vital step in many machine learning and data mining tasks. Feature selection can reduce the dimensionality, speed up the learning process, and improve the performance of the learning models. Most of the existing feature selection methods try to find the best feature subset according to a pre-defined feature evaluation criterion. However, in many real-world datasets, there may exist many global or local optimal feature subsets, especially in the high-dimensional datasets. Classical feature selection methods can only obtain one optimal feature subset in a run of the algorithm and they cannot locate multiple optimal solutions. Therefore, this paper considers feature selection as a multimodal optimization problem and proposes a novel feature selection method which integrates the barebones particle swarm optimization (BBPSO) and a neighborhood search strategy. BBPSO is a simple but powerful variant of PSO. The neighborhood search strategy can form several steady sub-swarms in the population and each sub-swarm aims at finding one optimal feature subset. The proposed approach is compared with four PSO based feature selection methods on eight UCI datasets. Experimental results show that the proposed approach can produce superior feature subsets over the comparative methods. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Feature Selection

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An Alternative-service Recommending Algorithm Based on Semantic Similarity

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China Communications 2017年第8期14卷 124-136页

作者： Kun Guo Yonghua Li Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingChina

With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.

关键词： activity recognition semantic model service recommendation unavailable service

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Energy Efficient UAV-Enabled Multicast Systems: Joint Grouping and Trajectory Optimization

Energy Efficient UAV-Enabled Multicast Systems: Joint Groupi...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Chang Deng Wenjun Xu Chia-Han Lee Hui Gao Wenbo Xu Zhiyong Feng Key Lab of Universal Wireless Communications Beijing University of Posts and Telecommunications Ministry of Education China National Chiao Tung University Taiwan Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning (C2ML) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one- dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the C2ML- enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA- TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline C2ML-enabled multicast grouping and the online CA-TSP inspired UAV- trajectory optimization, is shown to achieve excellent energy-saving performance.

关键词： Unmanned aerial vehicles Trajectory optimization Data communication Multicast communication Energy consumption

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Research on improve topic representation over short text 3

Research on improve topic representation over short text

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3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

作者： Li, Lingyun Sun, Yawei Han, Xu Wang, Cong Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing100876 China School of Software Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781538642108

According to the characteristics of sparseness, poor focus and lack of semantic information in short text, the existing studies mainly improved the topic representation from two aspects: Adjusting the structure of the topic model to increase word co-occurrence and incorporating word embedding to enrich the semantic information. In this paper, we review the existing topic representation methods for short text from these two aspects, select DMM, BTM and LF-DMM as targets and compare the quality of topic representations from them. Considering the traditional topic model does not incorporate word embedding, and single short text often lacks context information due to length limitation, we try to combine it with word embedding, we use two real-world datasets to evaluate the quality of topic representation in the classification task: Although the LF-DMM model incorporates word embedding, it performs poorly on short text, and the performance of DMM and BTM integrated with word embedding improve greatly. © 2018 IEEE.

关键词： Embeddings

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Codebook-Based Beam Tracking for Conformal Array-Enabled UAV MmWave Networks

arXiv

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arXiv 2020年

作者： Zhang, Jinglin Xu, Wenjun Gao, Hui Pan, Miao Han, Zhu Zhang, Ping Key Lab of Universal Wireless Communications Ministry of Education 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 Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

Millimeter wave (mmWave) communications can potentially meet the high data-rate requirements of unmanned aerial vehicle (UAV) networks. However, as the prerequisite of mmWave communications, the narrow directional beam tracking is very challenging because of the three-dimensional (3D) mobility and attitude variation of UAVs. Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks. More specifically, the key contributions of our work are three-fold. 1) A new mmWave beam tracking framework is established for the CA-enabled UAV mmWave network. 2) A specialized hierarchical codebook is constructed to drive the directional radiating element (DRE)-covered cylindrical conformal array (CCA), which contains both the angular beam pattern and the subarray pattern to fully utilize the potential of the CA. 3) A codebook-based multiuser beam tracking scheme is proposed, where the Gaussian process machine learning enabled UAV position/attitude predication is developed to improve the beam tracking efficiency in conjunction with the tracking-error aware adaptive beamwidth control. Simulation results validate the effectiveness of the proposed codebook-based beam tracking scheme in the CA-enabled UAV mmWave network, and demonstrate the advantages of CA over the conventional planner array in terms of spectrum efficiency and outage probability in the highly dynamic scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Millimeter waves

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A multiobjective genetic algorithm based dynamic bus vehicle scheduling approach 1

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13th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2018

作者： Shi, Hongyi Wang, Chunlu Zuo, Xingquan 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 China School of Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

Bus vehicle scheduling is very vital for bus companies to reduce operation cost and guarantee quality of service. Urban roads are easily blocked due to bad weather, such that it is significant to study the bus vehicle scheduling problem under traffic congestion caused by bad weather. In this paper, a dynamic bus vehicle scheduling approach is proposed, which consists of two parts: (1) generate a set of candidate vehicle blocks once the road is blocked;(2) adopt the non-dominated sorting genetic algorithm combined with a departure time adjusting process to select a subset of vehicle blocks from the candidate blocks set to form a vehicle scheduling scheme. Experiments show that our approach can significantly improve quality of service compared to the manual vehicle scheduling scheme. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Buses

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