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IEEE International Conference on Radar

作者： Xinyu Li Yuan He Xiaojun Jing Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728126609

ISBN: (纸本)9781728137858

In prior work, both person identification and activity classification in radar have been investigated. In this paper, we go one step beyond separate radar-based person identification and activity classification by proposing a deep multi-task network to complete the two tasks simultaneously. MOCAP dataset, from Carnegie Mellon University, is used for micro-Doppler signatures simulation. Six activities performed by six persons are adopted. 99.54% activity classification accuracy and 96.25% person identification accuracy is achieved with the network. To understand the networks impact on performance, ablation study is performed and experiment shows that each component in the network can facilitate the multi-task learning.

关键词： Task analysis Feature extraction Radar Kernel Legged locomotion Conferences Sports

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Mobile Robot Object Recognition in The Internet of Things based on Fog computing

Mobile Robot Object Recognition in The Internet of Things ba...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Meixia Fu Songlin Sun Kaili Ni Xiaoying Hou National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Mobile robot object recognition has attracted significant attention in the internet of things recently, in which there are many challenging tasks, such as the objects, the communication networks and the computer system. It still needs a large of computation, communication and storage capability for the whole system. In this paper, we propose a scheme of mobile robot object recognition in IOT and use edge nodes to process the data from robot vision instead of cloud computing. Besides, we adopt YOLOv3 as the main algorithm in the edge nodes to process the video data. The video from the camera on the robot is transmitted to the fog node by a Wi-Fi router. The advantages of using edge nodes for computation local robot clusters are reliability, real-time capability and flexibility compared with the cloud. In our experiment, we efficiently train the computer model using COCO database deployed to GPU. The robot using the proposed model could recognize the objects in real-time. We achieve mAP of 31.0% and response time of 52ms that illustrates the state-of-art performance of the proposed framework.

关键词： Object recognition Edge computing Cloud computing Real-time systems Servers Mobile robots

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A Weakness Relevance Evaluation Method Based on PageRank

A Weakness Relevance Evaluation Method Based on PageRank

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yanan Du Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

In security incidents, vulnerability is always caused by several weaknesses. But in the vulnerability assessment, most of studies did not take weakness into account, resulting in inaccurate assessment of vulnerability. In the Common Weakness Enumeration(CWE), the weakness is hierarchical, and for the hierarchical correlation of weaknesses, a propagation algorithm of weakness importance is proposed in this paper. The algorithm calculates the severity scores of each weakness based on the page ranking algorithm PageRank, and compares it with the expert evaluation results. The experimental results show that the algorithm can effectively evaluate the weakness.

关键词： Security Web pages Databases Software Standards Peer-to-peer computing

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Short Text Topic Recognition and Optimization Method for University Online Community 5th

Short Text Topic Recognition and Optimization Method for Uni...

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Wu, Xu Wu, Haitao Xie, Xiaqing Xu, Jin Zhang, Tianle Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cyberspace Security BUPT Beijing China Beijing University of Posts and Telecommunications Library Beijing100876 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

ISBN: (纸本)9783030242671

The university online community mainly records what happens in target areas and groups of people. It has the characteristics of timeliness, regional strong and clear target groups. Compared with Weibo and post-bar, university community’s text topic recognition needs to solve the problems of large text noise, fast text update and short single text content. To this end, this paper proposes a method of building university topic model based on LDA topic model. Through the steps of original text’s noise reduction, LDA (Latent Dirichlet Allocation (LDA), is a topic model commonly used in the field of machine learning and is often used for text categorization.) model recognition and weighted calculation of recognition results, etc., the event themes that characterize the common characteristics for university online community are obtained. Experiments based on real university online community’s data show that the topic model of university popular events established by the topic recognition model of this paper can reflect some popular events in colleges and universities, so as to provide reasonable support for university management. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

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Reinforcement Learning Based Antenna Selection in User-Centric Massive MIMO

Reinforcement Learning Based Antenna Selection in User-Centr...

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IEEE Conference on Vehicular Technology (VTC)

作者： Xinxin Chai Hui Gao Ji Sun Xin Su Tiejun Lv Jie Zeng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Ministry of Industry and Information Technology Institute of Telecommunications Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms.

关键词： Loaded antennas Machine learning algorithms Reinforcement learning Antenna arrays Massive MIMO

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Scalable Explanation of Inferences on Large Graphs

Scalable Explanation of Inferences on Large Graphs

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IEEE International Conference on Data Mining (ICDM)

作者： Chao Chen Yifei Liu Xi Zhang Sihong Xie Lehigh University Bethlehem PA USA Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education BUPT Beijing China

Probabilistic inferences distill knowledge from graphs to aid human make important decisions. Due to the inherent uncertainty in the model and the complexity of the knowledge, it is desirable to help the end-users understand the inference outcomes. Different from deep or high dimensional parametric models, the lack of interpretability in graphical models is due to the cyclic and long-range dependencies and the byzantine inference procedures. Prior works did not tackle cycles and make the inferences interpretable. We formulate the explanation of probabilistic inferences as a constrained cross-entropy minimization problem to find simple subgraphs that faithfully approximate the inferences. We prove that the optimization is NP-hard, while the objective is not monotonic and submodular to guarantee efficient greedy approximation. We propose a beam search algorithm to find trees to enhance the explanation interpretability and diversity. To allow efficient search on large and dense graphs without hurting faithfulness, we further propose parallelization and a pruning strategy. We demonstrate superior performance on four networks from distinct applications, comparing favorably to other explanation methods, including LIME.

关键词：

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Modulation Recognition based on Incremental Deep Learning

Modulation Recognition based on Incremental Deep Learning

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International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Yong Yang Menghan Chen XiaoYa Wang Piming Ma The 54th Research Institute of CETC Shijiazhuang China School of Information Science and Engineering Shandong University Qingdao China School of Information and Communication Engineering National Engineering Laboratory for Mobile Network Security Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications line Beijing China

Incremental learning has emerged to solve the problem of incrementally updating the classification model as the number of data classes grows. There are many challenges in incremental learning such as catastrophic forgetting and learning efficiency. In this paper, we present a method of the modulation recognition based on incremental learning, that allows learning continuously with a class-incremental way. The new classes can be added into the existing model progressively from a sequential data stream. We conduct experiments on the modulation signal dataset characterized by the constellation diagram, the experimental results prove the feasibility of our incremental learning system. Our method performs similarly in classification accuracy compared to common multi-task joint training, but performs better in training efficiency.

关键词： Training Learning systems Deep learning Constellation diagram Memory management Modulation Aerospace electronics

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Performance Analysis of Small Cell Networks Using Accurate Path Loss Model at 39 GHz 15

Performance Analysis of Small Cell Networks Using Accurate P...

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15th International Symposium on Wireless Communication Systems, ISWCS 2018

作者： Zhang, Yangying Jing, Xiaojun Gao, Hui Li, Bohan Xie, Jianxiao Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China

ISBN: (纸本)9781538650059

In this paper, we apply an accurate millimeter wave (mmWave) path loss model, obtained from outdoor measurement operated at 39 GHz in Qingdao, China, into the stochastic geometry analysis to investigate the system performance of small cell networks (SCNs) with non-identical user equipment (UE) - base station (BS) antenna heights. The analytical expressions for coverage probability and outage probability are derived in detail, which are functions of BS density and absolute difference between the antenna heights of BS and UE. The analytical results of coverage probability and outage probability are verified through Monte Carlo simulation. Our study shows that as the SCNs become denser, the impact of antenna heights difference on coverage and outage probability is more significant. © 2018 IEEE.

关键词： Millimeter waves

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Emergency analysis based on affective computing in universities forum 5th

Emergency analysis based on affective computing in universit...

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Lu Wu, Xu Xie, Xiaqing Xu, Jin Zhang, Tianle Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China School of Cyberspace Security BUPT Beijing100876 China Beijing University of Posts and Telecommunications Library Beijing100876 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

ISBN: (纸本)9783030242701

Emergency analysis for social networking sites have gained increased attention. In order to detect emergencies in University Forums, we present an emergency analysis method based on affective computing. The method detects the emergency and calculates the scores in real-time based on affective computing and the University Public Opinion Ontology (UPO_Ontology) established manually. The method is evaluated on two test datasets. The results show that the UPO_Ontology and affective computing greatly improved the accuracy, recall and F-measure. The method proposed in this paper meets the requirement of real-time detection. What's more, It gain of 1.24% in F-measure over the "event-specific detection" proposed by Laylavi et al. and outperforms emergency detect using contextual semantics by nearly 7.1% in F-measure. The analysis of the emergencies in University Forums will help Universities deal with emergencies more effectively and provide students with better service. © Springer Nature Switzerland AG 2019.

关键词： Semantics

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Intelligent reflecting surface aided multi-user millimeter-wave communications for coverage enhancement

arXiv

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arXiv 2019年

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

Intelligent reflecting surface (IRS) is envisioned as a promising solution for controlling radio propagation environments in future wireless systems. In this paper, IRS is exploited to extend the millimeter wave (mmW) signal coverage to blind spots, thereby reducing power consumption whilst improving communication performance. To merge IRS with mmW communications enjoying gigabit data-rate, we introduce a distributed IRS aided mmW system to support multi-user transmission. Taking into account the multi-user interference, we study a joint active and passive beamforming problem for weighted sum-rate maximization. Then, an alternating iterative algorithm with closed-form expressions is proposed to tackle the non-convex problem. Moreover, we design a quantitative projection method for the IRS with discrete phase shifts. Finally, the simulation results demonstrate that the distributed IRS can effectively support multi-user mmW transmissions based on our proposed algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Millimeter waves

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