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1st International Conference on Security and Privacy in Digital Economy, SPDE 2020

作者： Deng, Xiaolong Dai, Zhengge Sun, Mingdong Lv, Tiejun Key Lab of Trustworthy Distributed Computing and Service of Education Ministry Beijing University of Posts and Telecommunications Beijing100876 China BeiJing THUNI Soft Corporation Limited Beijing100084 China

ISBN: (纸本)9789811591280

With the development of Internet and online social communication tools, malicious social bots have become a problem which cannot be ignored. They are intentionally manipulated by some organizations or people and always disseminate malicious information on the Internet, which greatly impact network environment. As a result, the detection of malicious social bots has become a hot topic in machine learning and many kinds of classification methods are widely used to detect social bots. However, the current classification methods are limited by the unbalanced dataset in which the samples of human users always outnumber samples of social bots. Given by the nature of binary classification problem of detecting social bots, the imbalance of the dataset greatly impacted the classification accuracy of the social bots. Aiming to promote the classification accuracy, this article has proposed the use of Variational Autoencoder (VAE) to generate samples of social bots basing on existing samples and thus mitigate the problem of imbalance dataset before feeding the original dataset into classifiers. In this way, the classification accuracy of social bots and normal human users can be efficiently improved basing on selected six categories of user account features. In order to verify the advantage of VAE in generating social bot samples, other traditional oversampling algorithm named SMOTE and SMOTE based SVM are also used to generate samples of social bots and compared in the contrast experiments. The experimental results indicated that the proposed method in this article has achieved better classification accuracy compared with other scenarios using the same datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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RBCP: A Reputation-based Blockchain Consensus Protocol for Internet of Things

RBCP: A Reputation-based Blockchain Consensus Protocol for I...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Jiaze Shang Tianbo Lu Yingjie Cai Shuang Luo Zhaoxin Jin School of Computer Science (National Pilot Software Enginerring School) 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

The application of blockchain in the Internet of Things (IoT) solves the problem of centralization, enables data interaction of IoT nodes in untrusted environments. However, most of the blockchain consensus protocols are computationally expensive and poorly scalable, which cannot meet the current demand for low power consumption and low latency of IoT devices. Meanwhile, IoT nodes suffer from many malicious attacks. An effective reputation evaluation scheme is valuable for establishing a trusted, secure IoT environment with certain applications. In this paper, we propose RBCP, a novel reputation-based blockchain consensus protocol, and apply it to the IoT. The protocol divides IoT nodes into multiple regions, and consensus nodes in each region are selected based on real-time updated reputation values, which are used to participate in light PoW consensus and finally propose blocks. Then, we model the deposits and profits of the nodes as Stackelberg game and analyze the relationship between them. To evaluate our proposed protocol, we compare it with existing consensus protocols through several experiments. Our experimental results show that RBCP has advantages in terms of TPS, consensus time, and communication consumption, proving that it has low latency and less consensus time under the premise of sufficient security.

关键词： Time-frequency analysis Smart cities Scalability Games Real-time systems Consensus protocol Behavioral sciences

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VMScan: an out-of-VM malware scanner

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The Journal of China Universities of Posts and Telecommunications 2020年第4期27卷 59-68页

作者： Lin Jie Liu Chuanyi Fang Binxing School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Information and Electronic Engineering Chinese Academy of EngineeringBeijing 100088China

The harm caused by malware in cloud computing environment is more and more serious. Traditional anti-virus software is in danger of being attacked when it is deployed in virtual machine on a large scale, and it tends not to be accepted by tenants in terms of performance. In this paper, a method of scanning malicious programs outside the virtual machine is proposed, and the prototype is implemented. This method transforms the memory of the virtual machine to the host machine so that the latter can access it. The user space and kernel space of virtual machine memory are analyzed via semantics, and suspicious processes are scanned by signature database. Experimental results show that malicious programs can be effectively scanned outside the virtual machine, and the performance impact on the virtual machine is low, meeting the needs of tenants.

关键词： security virtualization cloud malware virus detection signature scanning

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Base Station Network Traffic Prediction Approach Based on LMA -DeepAR 6

Base Station Network Traffic Prediction Approach Based on LM...

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6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021

作者： Zhang, Jiachen Zuo, Xingquan Xu, Mingying Han, Jing Zhang, Baisheng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing Uinversity of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing Uinversity of Posts and Telecommunications Beijing China Network Interface Virtualization Shanghai System Design Deptment Zhongxing Telecommunication Equipment Corporation Shanghai China

ISBN: (纸本)9780738126043

Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the network traffic nonlinear and nonstationary, which brings challenges to the long-term prediction of network traffic. In this paper, the traffic model LMA-DeepAR for base station network is established based on DeepAR. Acordding to the distribution characteristics of network traffic, this paper proposes an artificial feature sequence calculation method based on local moving average (LMA). The feature sequence is input into DeepAR as covariant, which makes the statistical characteristics of network traffic near a period of time in the past be considered when updating parameters, and the interference of non-stationary network traffic on model training will be reduced. Experimental results show that the proposed prediction approach (LMA-DeepAR) outperforms other methods in the overall long-term prediction performance and stability of multi cell network traffic. © 2021 IEEE.

关键词： Base stations

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Energy Efficiency Maximization in Massive MIMO-NOMA Networks with Non-linear Energy Harvesting

Energy Efficiency Maximization in Massive MIMO-NOMA Networks...

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IEEE Conference on Wireless Communications and Networking

作者： Zhongyu Wang Tiejun Lv Weicai Li 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

This paper investigates the energy-efficient resource allocation problem in massive multiple input and multiple output (MIMO) non-orthogonal multiple access (NOMA) networks with practical non-linear energy harvesting (NEH). In the considered system, a multi-antenna base station (BS) transfers power to the Internet-of-Things (IoT) devices via energy beamforming in the downlink, followed by the IoT devices sending their data simultaneously in the uplink by consuming the harvested energy. A time division protocol is designed to adequately allocation the energy harvesting (EH) time and wireless information transmission time. To improve the energy efficiency (EE), we propose a joint power, time and antenna selection allocation scheme under the NEH model. An EE maximization problem is formulated to effectively determine the optimal resource allocation strategies. As the formulated problem is non-trivial, a non-linear fraction programming method is applied to convert the problem into a convex optimization problem, and then solve it by Lagrange dual decomposition approach. Simulation results demonstrate the effectiveness of the proposed solution.

关键词： Wireless communication NOMA Simulation Programming Energy efficiency Convex functions Resource management

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Enabling Media-Based Modulation for Reconfigurable Intelligent Surface Communications

Enabling Media-Based Modulation for Reconfigurable Intellige...

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IEEE Conference on Wireless Communications and Networking

作者： Yang Yan Yashuai Cao Tiejun Lv 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

Reconfigurable intelligent surface (RIS) is highly promising to be applied to enable media-based modulation (MBM) transmissions, which is expected to greatly reduce the hardware cost at the transmitter. In this paper, we propose the two MBM schemes for RIS communications, which are non-differential and differential schemes. In the non-differential scheme, we utilize the phase variations of the RIS to achieve a higher transmission rate compared to the conventional MBM schemes with radio frequency (RF) mirrors. Particularly, in the differential scheme, we decompose the space time block (STB) matrix into a phase state space (PSS) matrix and a selection-permutation (SP) matrix for enhancing the flexibility of differential modulation. The proposed treatment breaks through two inherent design limitations in the traditional differential space time block code (DSTBC). Moreover, the upper bound on the average symbol error rate (SER) of both schemes are derived. Finally, the theory and simulation results show that our proposed schemes can achieve higher transmission rates than the benchmark schemes.

关键词： Radio frequency Upper bound Spectral efficiency Simulation Reconfigurable intelligent surfaces Hardware Matrix decomposition

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Coordinate Attention-Based Convolution Neural Network for In-loop Filter of AVS3

Coordinate Attention-Based Convolution Neural Network for In...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Ruotong Wu Songlin Sun Jiaqi Zou Shaokang Wang Zhilei Ling School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Employing deep learning is a promising solution for reducing encoding bit rate in future video encoding systems. This paper proposes a neural network-based in-loop filter for the third generation of Audio Video Coding Standard (AVS3). The proposed network introduces a coordinate attention mechanism-based convolutional neural network in-loop filter (AMCNNLF) with a flexible attention module and a residual feature aggregation (RFA) module. Specifically, the attention module focuses on salient features to capture the visual structure, while the RFA module takes full advantage of the local refinement features. By leveraging the encoding parameters, we introduce the Quantizer Parameter (QP) values as auxiliary features to enable the proposed network suitable for processing encoded videos with multiple QPs. Experimental results indicate that the proposed network reduces the average Bjøntegaard-Delta rate (BD-Rate) of luma component about 0.4% under all intra configuration compared with the benchmark.

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A New Semantic Segmentation Diagram for Intelligent Transportation Based on Heterogeneous Knowledge Base

A New Semantic Segmentation Diagram for Intelligent Transpor...

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IEEE Conference on Wireless Communications and Networking

作者： Jingyuan Tang Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Semantic segmentation is regarded as an important technology for future communication and sensing networks due to its promising ability to extract features of transmit data. It integrates the functionality of computer vision and can realize high-fidelity transmission in the channel with lower bandwidth. Knowledge Base (KB) is a key component for the semantic segmentation framework. In this paper, we consider the scenario of intelligent transportation and propose a heterogeneous KB to extract the to-be-transmitted features and information at multiple levels, i.e., the raw level, symbol level, feature level and image level. The compressed features are obtained by a deep-learning-based framework. The proposed KB is shared with the transmitter and the receiver, by a service-oriented multi-stream transmission algorithm to meet various requirements of services. Experiments results demonstrate significant performance gain in terms of quality of service and encoding efficiency.

关键词： Semantic segmentation Semantics Knowledge based systems Transportation Symbols Object detection Streaming media

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The Relationship between Information Dissemination Channels, Health Belief, and COVID-19 Vaccination Intention: Evidence from China

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Journal of environmental and public health 2023年第1期2023卷 6915125-6915125页

作者： Huang, Chuanwu Yan, Dongqi Liang, Shuang School of Digital Media and Design Arts Beijing University of Post and Telecommunication 100088 Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education 100088 Beijing China Beijing Key Laboratory of Network System and Network Culture 100876 Beijing China

In the context of the ongoing global epidemic of COVID-19 and frequent virus mutations, the implementation of vaccine is the key to the prevention and control of the epidemic at this stage. In order to provide recommendations and evidence to support global epidemic prevention and control and vaccination efforts from the perspectives of health communication and individual psychological perceptions and to improve the vaccination rate of COVID-19 vaccine among appropriate populations, this study conducted a questionnaire survey in eight districts of Beijing and collected a total of 525 valid data points. A health belief model was used to examine the predictors of COVID-19 vaccination behavior, and the relationship between different COVID-19 vaccine information dissemination channels, residents' health beliefs, and propensity to vaccinate was analyzed. This study found the following: (1) among new media, interpersonal communication and traditional media communication channels, the new media channel had the largest number of audiences;(2) the personal health beliefs of audiences in the three information channels differed significantly, with the highest perceived benefits and lowest perceived barriers in the interpersonal communication channel and the highest perceived barriers in the new media communication channel;(3) the health belief model was a significant predictor, with perceived benefits and barriers being the most effective attitudinal variables for predicting vaccination intention. This study is valuable for advancing and improving vaccine communication diffusion research and promoting wider application of the health belief model and communication media in health communication topics. Copyright © 2023 Chuanwu Huang et al.

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Chinese Open Relation Extraction with Pointer-Generator Networks

Chinese Open Relation Extraction with Pointer-Generator Netw...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Ziheng Cheng Xu Wu Xiaqing Xie Jingchen Wu Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Most of the traditional Chinese open relation extraction (Open RE) system exploit the syntactic, lexical and other language structure information obtained by natural language processing (NLP) tools from sentences to build hand-crafted patterns for extraction, which is easy to cause error propagation and affect the accuracy of extraction. In this paper, we propose an end-to-end abstract Chinese Open RE model based on the Pointer-Generator network, PGCORE. We employ the results extracted by the state-of-the-art pattern-based Chinese Open RE system as the training set of the model. Experimental results show that our method is outperforms the pattern-based several baselines system, which proves the feasibility and effectiveness of using deep learning models for Chinese Open RE.

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