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LRChain:data protection and sharing method of learning archives based on consortium blockchain

The Journal of China Universities of Posts and Telecommunications 2024年第4期31卷 28-42页

作者： Lan Lina Gao Yuhan Shi Ruisheng Wu Fenfen School of Humanities Beijing University of Posts and TelecommunicationsBeijing 100876China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and TelecommunicationsMinistry of EducationBeijing 100876China

Learning archives management in traditional systems faces challenges such as inadequate security,weak tamper resistance,and limited sharing *** address these issues,this paper proposes LRChain,a method based on consortium blockchain,for lifelong learning archives data protection and *** employs a combination of on-chain and off-chain cooperative storage using a consortium chain and inter planetary file system(IPFS)to enhance data security and *** also enables fine-grained verification of learning archives through selective disclosure principles,ensuring privacy protection of sensitive ***,an attributebased encryption(ABE)algorithm is utilized to establish authorized access control for learning archives,facilitating safe and trusted *** evaluations and security analyses demonstrate that this method exhibits decentralization,strong security,tamper resistance,and performs well,effectively meeting the requirements for secure sharing of learning archive data.

关键词： learning archives consortium blockchain collaborative storage secure sharing

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Machine-learning based ocean atmospheric duct forecasting:a hybrid model-data-driven approach

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The Journal of China Universities of Posts and Telecommunications 2023年第4期30卷 1-9页

作者： Feng Yuting Gong Haobing Hao Xiaojing Gao Hui Guo Xiangming Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Radio Wave Propagation Characteristics and Modeling Technology 22nd Research Institute of China Electronics Technology CorporationQingdao 266107China

The atmospheric duct is a vital radio wave *** methods of forecasting the atmospheric duct mainly include statistical analysis based on sounding observation data and mesoscale numerical model-based *** former can provide accurate duct information but is highly dependent on the acquisition of data *** latter is more practical but still lacks *** paper introduces machine learning to establish a novel meteorological parameter correction model for atmospheric duct *** detail,using the weather research and forecasting(WRF)model data and spatiotemporal characteristics as input,sounding data as label and extreme gradient boosting(XGBoost)model for training,the meteorological parameter correction effect is the best,i.e.,the accuracy of forecast meteorological parameters is improved by about 65.4%.Combining the mapping relationship between meteorological parameters and corrected atmospheric refractive index(CARI),and the transition mechanism of CARI to duct parameters,a new duct forecasting mechanism is *** to the high efficiency of numerical model and the accuracy of sounding data,the new duct forecasting mechanism has excellent *** comparing the duct forecasting results,the forecasting accuracy of the new duct forecasting model is significantly higher than that of the mesoscale model.

关键词： marine atmospheric duct neural network fitting machine learning forecasting mechanism

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Robust Spatio-Temporal Trajectory Modeling Based on Auto-Gated Recurrent Unit 19

Robust Spatio-Temporal Trajectory Modeling Based on Auto-Gat...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Jia, Jia Li, Xiaoyong Li, Ximing Li, Linghui Yuan, Jie Wang, Hongmiao Gao, Yali Qiu, Pengfei Tang, Jialu Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350346558

With the huge amount of crowd mobility data generated by the explosion of mobile devices, deep neural networks (DNNs) are applied to trajectory data mining and modeling, which make great progresses in those scenarios. However, recent studies have demonstrated that DNNs are highly vulnerable to adversarial examples which are crafted by adding subtle, imperceptible noise to normal examples, and leading to the wrong prediction with high confidence. To improve the robustness of modeling spatiotemporal trajectories via DNNs, we propose a collaborative learning model named 'Auto-GRU', which consists of an autoencoder-based self-representation network (SRN) for robust trajectory feature learning and gated recurrent unit (GRU)-based classification network which shares information with SRN for collaborative learning and strictly defending adversarial examples. Our proposed method performs well in defending both white and black box attacks, especially in black-box attacks, where the performance outperforms state-of-the-art methods. Moreover, extensive experiments on Geolife and Beijing taxi traces datasets demonstrate that the proposed model can improve the robustness against adversarial examples without a significant performance penalty on clean examples. © 2022 IEEE.

关键词： Deep neural networks

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Real Time Attrition Prediction Mechanism Based on Deep Learning 2

Real Time Attrition Prediction Mechanism Based on Deep Learn...

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2nd International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2022

作者： Wu, Chen Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9781665492713

Modern companies mostly have their own employee management systems, which retain a large amount of employee behavior data. These data can be used to mine valuable information, such as whether employees have attrition intentions. This paper proposes a mechanism of attrition prediction based on deep learning, which mainly includes three modules: (1) Using LSTM to build a performance-attrition pre-trained model;(2)Using Transformer to build a sentiment tendency model based on employee weekly reports;(3) Based on (1), (2), establishing Real Time Attrition Prediction Mechanism in the company's employee management system to predict employees' willingness to leave at any time. The purpose of this mechanism is to help companies manage better and reduce losses caused by employee attrition. © 2022 IEEE.

关键词： Forecasting

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REINFORCEMENT LEARNING BASED TIME-DOMAIN MUTUAL INTERFERENCE AVOIDANCE FOR AUTOMOTIVE RADAR

REINFORCEMENT LEARNING BASED TIME-DOMAIN MUTUAL INTERFERENCE...

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IET International Radar Conference 2023, IRC 2023

作者： Xiao, He Wang, Jianping Li, Runlong He, Yuan Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Faculty of EEMCS Delft University of Technology Delft Netherlands

ISBN: (纸本)9781839539954

Due to the extensive usage of automotive radars on vehicles, mutual interference among radars on the road is becoming considerable. To address this, we propose a time domain strategy based on deep reinforcement learning (DRL). This approach helps avoid mutual interference for automotive radars in the time domain without extra communications. The numerical simulation results demonstrate that the proposed approach can avoid interference as effectively as frequency hopping. Moreover, the time domain strategy has more advantages than frequency hopping when encountering dynamic interference. © The Institution of Engineering & Technology 2023.

关键词： Frequency hopping

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LayerCFL:an efcient federated learning with layer-wised clustering

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Cybersecurity 2025年第1期8卷 72-85页

作者： Jie Yuan Rui Qian Tingting Yuan Mingliang Sun Jirui Li Xiaoyong Li School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijingChina Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijingChina Institute of Computer Science Faculty of Mathematics and Computer ScienceUniversity of GoettingenGöettingenGermany School of Information Technology Henan University of Chinese MedicineZhengzhouHenanChina

Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model *** address this challenge,prior research has introduced clustered FL(CFL),which involves clustering clients and training them *** its potential benefts,CFL can be computationally and communicationally expensive when the data distribution is unknown *** is because CFL involves the entire neural networks of involved clients in computing the clusters during training,which can become increasingly timeconsuming with large-sized *** tackle this issue,this paper proposes an efcient CFL approach called LayerCFL that employs a Layer-wised clustering *** LayerCFL,clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental *** experimental results demonstrate the efectiveness of LayerCFL in mitigating the impact of Non-IID data,improving the accuracy of clustering,and enhancing computational efciency.

关键词： Federated learning Clustered federated learning Non-IID Layer-wised clustering

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Cross-Lingual Propaganda Detection

Cross-Lingual Propaganda Detection

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Zhang, Wenshan Zhang, Xi Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9781665480451

Propaganda information that aims at influencing people's opinions is becoming more and more common around people, especially through social media. For the detection of propaganda content, researchers have conducted studies from multiple perspectives, including document level, sentence level and fragment level. However, most existing methods have mainly focused on propaganda detection in a single language because of the lack of multilingual datasets. In this article, we first propose a Chinese and English propaganda detection dataset. Then we propose a cross-lingual propaganda detection method, using semantic and syntactic information between different languages. Extensive experiments on our multilingual dataset demonstrate the effectiveness of our proposal in cross-lingual propaganda detection. © 2022 IEEE.

关键词： Semantics

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Leveraging Multi-aspect Semantics for Conversation Structure Modeling 7

Leveraging Multi-aspect Semantics for Conversation Structure...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Guo, Tuwen Zhang, Xi Qiu, Lirong Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

With the advent of online chatting systems, conversation structure modeling has attracted increasing attention and can be beneficial to many downstream tasks such as topic summarization and dialog generation. Existing studies mainly focus on modeling the pairwise or thread-level text semantics in multiple interleaved conversations, and don't fully utilize the rich semantics such as the dialogue actions and speaker relationships. For this purpose, we propose to leverage broader semantics to better disentangle the conversations. Concretely, we devise a novel deep neural networks to capture the text contents, discourse structures and speaker attention in one framework, to better identify the utterance pairs with the 'reply-to' relationships. We evaluate our framework on two datasets, the publicly available Ubuntu IRC dataset and our proposed WeChat & QQ dataset. The results show that our model significantly outperforms the state-of-the-art baselines. © 2022 IEEE.

关键词： Semantics

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Incorporate Personas and Emotions into Dialog Generation 5

Incorporate Personas and Emotions into Dialog Generation

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5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023

作者： Wu, Yufeng Liu, Chengyu Guo, Wenjian Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350357738

Since the Turing test, creating believable open-domain dialogues has been a basic problem in the study of artificial intelligence. Achieving success with dialogue models can greatly improve natural human-computer interactions and open up new possibilities for applications such as chatbots for therapy. Incorporating emotional information into dialogue systems and giving them personas can enhance user satisfaction by fostering more natural communication and human-like conversations. Both of these study directions are comparatively well-established, with important accomplishments in them. On the other hand, various roles in real-life discussion scenarios portray emotions in different ways. In order to create more fulfilling and human-like discussions, we investigate in this study the integration of personas and emotions in dialogue creation systems. Through the combination of explicit and implicit persons, we tackle the problem of individualized and emotional conversation. While implicit personas are gleaned from linguistic style, colloquial idioms, and personality attributes, explicit personas are derived from user profiles. Explicit personas and emotional labels are incorporated into the dialogue agent, and implicit personas are captured through the use of latent variables in a pre-trained Transformer model. Our suggested method enhances the authenticity of discussions and user experience by producing responses that are consistent with personas and emotions, hence improving the quality of communication. © 2023 IEEE.

关键词： Human computer interaction

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Multiple Vandermonde Decomposition Based Channel Estimation for Mm Wave MIMO High-Mobility Communication in 5G and Beyond

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China Communications 2023年第4期20卷 12-25页

作者： Zhao Yi Xiaojun Jing School of Electronics and Information Hunan First Normal UniversityChangsha 410205China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Millimeter wave(mmWave)massive massive multiple input multiple output(MIMO)technique has been regarded as the viable solution for vehicular communications in 5G and *** achieve the substantial increase in date rates,it is important to take an effective channel state information(CSI).However,existing channel estimation strategies are unavailable since the users *** solve above issues,in this paper,inspired by a specific antenna structure,we propose a novel approach for fast time-varying channel ***,by considering the vehicle scenario with high-mobility,a corresponding mathematical model is firstly ***,based on the special structural of the sparse array,the switch network is used to replace the convention phase shifter of mmWave hybrid system,which can effectively reduce the number of radio-frequency(RF)chains and ***,by solving the semidefinite programming(SDP)duality problem,the Doppler frequency and path parameters are effectively *** results are shown that the computational complexity and estimation accuracy of the proposed algorithm is superior than that of the traditional schemes.

关键词： MIMO mmWave channel estimation high-mobility time-variant

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