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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Wenqing, Li Wenjie, Dong Xu, Zhang Yueming, Lu School Of Cyberspace Security Beijing University Of Posts And Telecommunications Beijing China Beijing Aerospace Automatic Control Institute Beijing China Beijing University Of Posts And Telecommunications Ministry Of Education Key Laboratory Of Trusted Distributed Computing And Service Beijing China

ISBN: (纸本)9781665418157

In order to solve the problems that private information is easy to be leaked and privacy violations are difficult to be traced in the process of data cross-domain transferring, we propose a blockchain-based controlled sharing mechanism for private data, which has the merits such as decentralization, tamper-proof, and traceability. By combining smart contract technology and cryptographic algorithms, we realize the user authentication and private data rights determination. On the basis of that, we extract privacy protection metadata by using technologies such as hash function and digital signature and then store it on the blockchain, which realizes the depositing and tracing of data transferring records. At the same time, we encrypt the original data and store it in a distributed file system to alleviate the problem of limited storage space in blockchain while ensuring the security of data storage. © 2021 IEEE.

关键词： Data privacy

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QPSK pilot-aided carrier phase recovery algorithm for 800-Gb/s/? DP-256QAM transmission

QPSK pilot-aided carrier phase recovery algorithm for 800-Gb...

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2020 Asia Communications and Photonics Conference, ACPC 2020/International Conference on Information Photonics and Optical Communications, IPOC 2020

作者： Cui, Han Xu, Xuekai Liu, Shuangyue 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 Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

A QPSK pilot-aided carrier phase recovery algorithm is investigated for 800-Gb/s/? DP-256QAM transmission systems. Simulation results show that, compared with BPS, this proposed algorithm reduces by 99% computational ... 详细信息

ISBN: (纸本)9781557528209

关键词： Quadrature phase shift keying

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Unsupervised Domain Adaptation for Human Activity Recognition in Radar

Unsupervised Domain Adaptation for Human Activity Recognitio...

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IEEE National Conference on Radar

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

ISBN: (数字)9781728189420

ISBN: (纸本)9781728189437

The difficulty of obtaining large-scale labeled radar data hinders the generalization of supervised deep learning algorithms, which makes the performance of these approaches drop when applied to a new domain. In this paper, we propose an unsupervised adversarial domain adaptation method with radar micro- Doppler spectrograms for human activity recognition. An improved adversarial loss, which has the same mechanism as Generative Adversarial Network, is employed for optimization. Besides, a multi-layer domain discriminator is presented in this method. A simulated dataset (source domain) with labeled radar spectrograms and a measured dataset (target domain) with unlabeled spectrograms are utilized for experiment. Experimental results demonstrate that the proposed method has good performance (with an average accuracy of 81.6%) on recognizing human activities without labeled measured spectrograms in the target domain. And comparison experiments with several state-of-the-art domain adaptation methods also indicate the efficiency of the proposed model.

关键词： Radar Feature extraction Spectrogram Adaptation models Training Task analysis Radar measurements

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An Effective Encrypted Traffic Classification Method Based on Pruning Convolutional Neural Networks for Cloud Platform

An Effective Encrypted Traffic Classification Method Based o...

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International Conference on Electronics, Communications and Information Technology (CECIT)

作者： Zilong Han Erxia Li Shichao Li Xiaoyong Li Chaoqun Kang Ruiwen Deng Yali Gao State Grid Shanghai Energy Interconnection Research Institute Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665437585

With the rapid development of cloud computing technology, the traffic of cloud platform also increases rapidly. Most of these traffic adopt encryption technology, the original traffic classification method is no longer effective. In this paper, we propose an efficient encrypted traffic classification method based on pruning convolution neural network: PCNN. This method does not need to extract features manually, but automatically extracts high-level features through CNN, which makes the model have generalization ability and reduces the cost. Then the pruning technology is adopted to prune the model and retain the parameters that are highly important to the model, which greatly reduces the size and computation of the model and will not affect the performance of the model. We used the public network dataset ISCXVPN2016 to verify our proposed method, the experiments show that the F1 score of our proposed approach is 93% which is improved by 3%, and the precision is 94%, which is improved by 5%.

关键词： Cloud computing Computational modeling Data preprocessing Neurons Telecommunication traffic Feature extraction Data models

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QPSK-assisted MIMO equalization for 800-Gb/s/? DP-256QAM systems

QPSK-assisted MIMO equalization for 800-Gb/s/? DP-256QAM sys...

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2020 Asia Communications and Photonics Conference, ACPC 2020/International Conference on Information Photonics and Optical Communications, IPOC 2020

作者： Liu, Siqi Liu, Shuangyue Tang, Xizi Guo, Mengqi 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 Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing100876 China

A QPSK-assisted MIMO equalization is investigated to compensate bandwidth limitation for 800-Gb/s/? DP-256QAM systems with only 25G-class optics. Compared with conventional MIMO equalization, the proposed equalization... 详细信息

ISBN: (纸本)9781557528209

关键词： Quadrature phase shift keying

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding for Next-POI Recommendation

Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-grap...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoqian Liu Xiuyun Li Yuan Cao Fan Zhang Xiongnan Jin Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Chengde China Hebei Normal University for Nationalities Chengde China Knowledge Discovery and Data Mining Research Center Zhejiang Lab Hangzhou China

Next-POI recommendation aims to explore from user check-in sequence to predict the next possible location to be visited. Existing methods are often difficult to model the implicit association of multi-modal data with user choices. Moreover, traditional methods struggle to fully explore the variation of user preferences at variable time intervals. To tackle these limitations, we propose a Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding approach (Mandari). We first construct a novel Multi-Modal Temporal Knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge mining method to model the segmentation and periodicity of user check-in and obtain temporal prediction probability. Finally, we fuse temporal prediction probability with the previous sub-graph prediction probability to obtain the final result. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.

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Chinese open relation extraction with pointer-generator networks 5

Chinese open relation extraction with pointer-generator netw...

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5th IEEE International Conference on Data Science in Cyberspace, DSC 2020

作者： Cheng, Ziheng Wu, Xu Xie, Xiaqing Wu, Jingchen Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Library Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728195582

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. © 2020 IEEE.

关键词： Extraction

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Robust Text Clustering with Graph and Textual Adversarial Learning

Robust Text Clustering with Graph and Textual Adversarial Le...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yongming Liang Tian Tian Kaifu Jin Xiaoyu Yang Yuefei Lv Xi Zhang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Text clustering is a fundamental task that finds groups of similar texts in the corpus. Deep learning based models can capture the semantic and syntactic information in local word sequences to represent the texts but may ignore global text structures in the corpus. Recently, Graph Convolutional Networks (GCNs) have achieved the state-of-the-art performance in many applications, which may facilitate the text clustering task by exploiting the graph structures between texts via words and phrases. However, GCNs are vulnerable to adversarial attacks, e.g., small perturbations in graph structures and node attributes lead to poor performance. To address this issue, we propose a Robust Text Clustering (RTC) approach which consists of both autoencoder and GCN modules to exploit the local text features and global text structures respectively in one unified framework. We generate two types of adversarial perturbations targeting text structure and graph structure, and develop an adversarial training method to improve the robustness and generalization of the RTC framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposal in text clustering.

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Real-time event detection and tracking in microblog via text chain and sentiment time series 5

Real-time event detection and tracking in microblog via text...

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5th IEEE International Conference on Data Science in Cyberspace, DSC 2020

作者： Piao, Bingxu Wu, Xu Wu, Jingchen Xie, Xiaqing Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Library Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728195582

Microblog becomes one of the most popular media for sharing first-hand information. As more close to real event, detecting and tracking event in microblogs is a research hotspot. To alleviate feature sparsity problem, traditional event detection and tracking methods exploit comments, replies, and reposts to enrich the information of posts. However, ignoring text relationship only leads to more irrelevant information. Aiming at this issue, a novel real-time event detection and tracking method for microblog stream is proposed. For each microblog from the real-time stream, similarity against historical event set is first evaluated. Then, known event microblogs are partitioned by the proposed method sentiment time series, clustered based on word relation graph, and compare with the previous graph to track burst keywords. Meanwhile, text chain is introduced to detect events for unknown event microblogs. Text chain is a unique relationship of microblogs, in which comments, replies, and reposts are used to form a word relation graph of the corresponding post. Posts and attached graphs are clustered to detect new events. Experiments are performed on real-time microblog datasets to evaluate the effectiveness of methods. Results show that the proposed method gains better F-measure than similar methods. © 2020 IEEE.

关键词： Time series

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