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IEEE Globecom Workshops

作者： Xing Ding Changgen Peng Hongfa Ding Maoni Wang Hui Yang Qinyong Yu College of Computer Science & Technology State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Information Guizhou University of Financial and Economics Guiyang China National Engineering Laboratory Big Data Application on Improving Government Governance Capabilities Guiyang China CETC Big Data Research Institute Co. Ltd Guiyang China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Behavior-based identity authentication has been of research interests due to its low cost and the fact that such authentication factors cannot easily copied nor stolen. However, single behavior feature authentication usuall cannot gaurantee sufficient accuracy. In this paper, a multi-factor behavior features identity authentication method is proposed by combining factors of mouse, keystroke and address. We apply support vector machine by using the information divergence of features as the weight of the linear kernel function to solve quantification's problem of features influence degree on authentication. The tailored support vector machine is applied to multi-factor behavior features to identity authentication. Our experimental results show that the improved support vector machine is better than both the linear and Gaussian kernel support vector machines for identity authentication in accuracy. The proppsed method can be applied to large volume users' identity authentication cases where their behavior features are likely to be similar.

关键词： Authentication Mice IP networks Feature extraction Support vector machines Hidden Markov models Presses

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Deep short text classification with knowledge powered attention

arXiv

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arXiv 2019年

作者： Chen, Jindong Hu, Yizhou Liu, Jingping Xiao, Yanghua Jiang, Haiyun Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China CETC Big Data Research Institute Co. Ltd. Guizhou China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Shuyan Technology Shanghai China Alibaba Group Zhejiang China

Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (CST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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YTCNet: A Real-time Algorithm for Parcel Damage Detection with Rich Features and Attention

YTCNet: A Real-time Algorithm for Parcel Damage Detection wi...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Zhi Chen Cuifeng Du Yuyu Zhou Haoxuan Guan Xiujie Huang Zhefu Li Changjiang Liu Xiaotian Zhuang Xingyu Zhu Quanlong Guan Jinan University Guangzhou China Guangdong-Macao Advanced Intelligent Computing Joint Laboratory China Cetc Potevio Science & Technology Co. Ltd Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving China Guangdong Testing Institute of Product Quality Supervision China Beijing JD Zhenshi Information Technology Co. Ltd Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this study, we tackle the challenge of parcel damage detection and present YTCNet to improve both accuracy and real-time performance. We enhance the Yolov5 algorithm by integrating C3TR modules into the architecture to capture more comprehensive feature information. Furthermore, we introduce the CBAM attention module to focus on critical areas in parcel images, enhancing the model’s feature extraction capabilities. By effectively combining C3TR and CBAM modules, we successfully developed an efficient parcel damage detection algorithm. Our experimental results demonstrate the superior performance of YTCNet in accuracy and real-time processing compared to various object detection models across multiple parcel damage scenarios. Our research significantly contributes to the logistics supply chain industry by aligning with IoT development. It enables practical applications to more effectively identify parcel damage, thereby enhancing customer satisfaction.

关键词： YOLO Industries Accuracy Supply chains Transportation Feature extraction Real-time systems

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A High-Speed Stable Polarization Encoder for Quantum key Distribution

A High-Speed Stable Polarization Encoder for Quantum Key Dis...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Ju Li Zexu Wang Changlei Wang Feifei Yin Kun Xu Huaxing Xu Yitang Dai State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China National Engineering Research Center for Public Safety Risk Perception and Control by Big Data China Academy of Electronics and Information Technology Beijing China CETC Academy of Electronics and Information Technology Group Co. Ltd. Beijing China

We report a high-speed polarization encoder which can be applied for quantum key distribution, resulting in an average bit error rate (BER) below 0.1% at a repetition frequency of 10 GHz under interference-free measur... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Frequency modulation Satellites Protocols Bit error rate Measurement uncertainty Sagnac interferometers Lasers and electrooptics

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A New Multi-Receiver Authentication Scheme for General Access Structure

A New Multi-Receiver Authentication Scheme for General Acces...

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IEEE International Symposium on Information Theory

作者： Jing Yang Shu-Tao Xia Xianfang Wang Can Xiang Fang-Wei Fu Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong China CETC Cyberspace Security Technology Co. Ltd. Beijing China College of Mathematics and Informatics South China Agricultural University Guangzhou Guangdong China Tianjin Key Laboratory of Network and Data Security Technology Chern Institute of Mathematics and LPMC Nankai University Tianjin China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

At present, existing multi-receiver authentication (MRA) schemes can only handle situations where the capabilities of all receivers are the same. However, in reality, different receivers may need to have different storage capabilities. In this paper, inspired by the secret sharing scheme based on the Chinese Remainder Theorem (CRT) for polynomial rings where distinct participants save shares with distinct sizes, we propose a new unconditionally secure MRA scheme for multiple messages by the same technique. As far as we know, our MRA scheme is the first MRA scheme with different storage capacities for different receivers and the first one based on the CRT for polynomial rings supporting general access structures. In contrast to the existing general MRA scheme, although our MRA scheme has more communication complexity, it has less computation complexity.

关键词： Structural rings Authentication Receivers Cathode ray tubes Polynomials Cryptography Computational complexity

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Aesthetic Attributes Assessment of Images

arXiv

引用

arXiv 2019年

作者： Jin, Xin Wu, Le Zhao, Geng Li, Xiaodong Zhang, Xiaokun Ge, Shiming Zou, Dongqing Zhou, Bin Zhou, Xinghui Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing100070 China Cetc Big Data Research Institute Co. Ltd. Guiyang Guizhou550018 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China SenseTime Research Beijing100084 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Peng Cheng Laboratory Beijing100191 China

Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describe the general aesthetic impression of an image using text. In this paper, we propose Aesthetic Attributes Assessment of Images, which means the aesthetic attributes captioning. This is a newformula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute. We introduce a new dataset named DPC-Captions which contains comments of up to 5 aesthetic attributes of one image through knowledge transfer from a full-annotated small-scale dataset. Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning and attention model in a single framework. The experimental results on our DPC-Captions and PCCD dataset reveal that our method can predict captions of 5 aesthetic attributes together with numerical score assessment of each attribute. We use the evaluation criteria used in image captions to prove that our specially designed AMAN model outperforms traditional CNN-LSTM model and modern SCA-CNN model of image captions. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

Weighted Linear Multiple Kernel Learning for Saliency Detect...

引用

2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

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Intelligent Attitude Control of Aircraft Based on LSTM

引用

IOP Conference Series: Materials Science and Engineering 2019年第1期646卷

作者： Bo Li Peixin Gao Xitong Li Daqing Chen School of Electronics and Information Northwestern Polytechnical University Shanxi Xi'an China CETC Key Laboratory of Data Link Technology Shanxi Xi'an China School of Engineering London South Bank University London UK

The flight attitude control is the core part of the maneuvering process in air combats. Traditional flight attitude control methods have high computational complexity, low flexibility and poor ability to learn sequential feature. This paper proposes a flight attitude control model based on long short term memory network, which utilizes its special gates structure to memorize historical information, and acquire the variation law of the attitude control variable from the time sequential data including the battlefield situation and flight parameters automatically. Moreover, the basic framework and training methods of the model are also introduced, and the influence caused by various LSTM network parameters is deeply discussed. The experiment results show that the proposed model has better prediction accuracy and convergence performance than the traditional recurrent neural network.

关键词：

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Efficient Sensing Parameter Estimation with Direct Clutter Mitigation in Perceptive Mobile Networks

arXiv

引用

arXiv 2024年

作者： Li, Hang Yang, Hongming Guo, Qinghua Zhang, J. Andrew Xiang, Yang Pang, Yashan The School of Electronics and Information Hangzhou Dianzi University Hangzhou310018 China The School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia The Global Big Data Technologies Centre University of Technology Sydney UltimoNSW2007 Australia National Key Laboratory of Advanced Communication Networks Academy of Network & Communications of CETC Hebei Shijiazhuang050081 China

In this work, we investigate sensing parameter estimation in the presence of clutter in perceptive mobile networks (PMNs) that integrate radar sensing into mobile communications. Performing clutter suppression before sensing parameter estimation is generally desirable as the number of sensing parameters can be significantly reduced. However, existing methods require high-complexity clutter mitigation and sensing parameter estimation, where clutter is firstly identified and then removed. In this correspondence, we propose a much simpler but more effective method by incorporating a clutter cancellation mechanism in formulating a sparse signal model for sensing parameter estimation. In particular, clutter mitigation is performed directly on the received signals and the unitary approximate message passing (UAMP) is leveraged to exploit the common support for sensing parameter estimation in the formulated sparse signal recovery problem. Simulation results show that, compared to state-of-the-art methods, the proposed method delivers significantly better performance while with substantially reduced complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Wireless networks

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

引用

Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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