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2020 IEEE Region 10 Conference, TENCON 2020

作者： Yao, Baoqi Gao, Hui Su, Xin Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (纸本)9781728184555

In recent years, human action recognition has received a considerable amount of research attention because of it's potential in a variety of applications, such as video surveillance, human-computer interaction, and virtual reality (VR). However, many researches on human action recognition performed in single-camera or double-camera system, which achieve reduced performance due to vulnerability to partial occlusion and miss-recognition of back. Some works on human action recognition use multiple cameras but are too complex for practical application. In this paper, we propose a new human action recognition system using triple Kinect sensors for VR application. Particularly, we design a mark detection method to determine the front of user and fusion skeleton data in real time. Features are extracted from three-dimensional (3D) skeleton data sequences, and divided into five parts according to body parts. A classification model based on the part-aware long short-term memory networks is proposed to recognize human motion. Finally, we demonstrate the system with a virtual reality basketball application and the results of experiment validate the feasibility of the proposed system. © 2020 IEEE.

关键词： Virtual reality

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A microservice based generic consensus engine for BaaS

A microservice based generic consensus engine for BaaS

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IEEE International Conference on Industrial Technology (ICIT)

作者： Huiyang Li Xu Zhang Jing Li Dongbin Wang Beijing University of Posts and Telecommunications Beijing China School of Cyber Security National Engineering Laboratory for Mobile Network Security Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing Tongbang Zhuoyi Technology Co. Ltd

ISBN: (纸本)9781728119496

Blockchain-as-a-service (BaaS) has gradually serve as an infrastructure, providing underlying blockchain services for the rapid deployment and transaction on-chain of large-scale enterprise application scenarios. By hosting different blockchains in its underlying structure layers, BaaS platform satisfies the requirements of consensus algorithms in different scenarios, and still uses its own consensus algorithms in different blockchains, which has limited scalability and is difficult to satisfy the requirements of future applications. To deal with such problems, MGCE, a microservice based generic consensus engine for BaaS is proposed in this paper. It is a functional framework based on microservices architecture which can load the appropriate consensus algorithm and perform the consensus process of transactions for blockchain nodes according the different scenarios. The engine is composed of Consensus Node Group, Consensus Management and four functional modules, namely Consensus Network, Transactions Pool, Consensus Protocol and Consensus Outcome. For microservice purpose, each functional module is deployed into a independently project, running in its own process, which is easy to modify and test, and interacts with other modules through the http APIs to complete the consensus process together. To test the function and performance of this engine, three common consensus algorithms have been implemented including Raft, BFT-SMART and PoW. Through experiments, it is proved that this engine could realize the loading and use of different consensus algorithms. Through the performance comparison between using the engine and with the direct use of each consensus algorithm, the results show that the overhead introducing the engine is acceptable. Through the operation result of three algorithms in different projects, the design of engine architecture could satisfy the microservice objective.

关键词： Costs Scalability Loading Microservice architectures Consensus algorithm Consensus protocol Engines

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Leveraging the Dual Capabilities of LLM: LLM-Enhanced Text Mapping Model for Personality Detection 39

Leveraging the Dual Capabilities of LLM: LLM-Enhanced Text M...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bi, Weihong Kou, Feifei Shi, Lei Li, Yawen Li, Haisheng Chen, Jinpeng Xu, Mingying School of Computer Science National Pilot School of Software Engineering BUPT Beijing 100876 China Key Laboratory of Trustworthy Distributed Computing and Service BUPT Ministry of Education Beijing 100876 China State Key Laboratory of Media Convergence and Communication CUC Beijing 100024 China State Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China School of Economics and Management BUPT Beijing 100876 China Beijing Technology and Business University Beijing 100048 China Xiangjiang Laboratory Changsha 410205 China North China University of Technology Beijing 100144 China

ISBN: (纸本)157735897X

Personality detection aims to deduce a user’s personality from their published posts. The goal of this task is to map posts to specific personality types. Existing methods encode post information to obtain user vectors, which are then mapped to personality labels. However, existing methods face two main issues: first, only using small models makes it hard to accurately extract semantic features from multiple long documents. Second, the relationship between user vectors and personality labels is not fully considered. To address the issue of poor user representation, we utilize the text embedding capabilities of LLM. To solve the problem of insufficient consideration of the relationship between user vectors and personality labels, we leverage the text generation capabilities of LLM. Therefore, we propose the LLM-Enhanced Text Mapping Model (ETM) for Personality Detection. The model applies LLM’s text embedding capability to enhance user vector representations. Additionally, it uses LLM’s text generation capability to create multi-perspective interpretations of the labels, which are then used within a contrastive learning framework to strengthen the mapping of these vectors to personality labels. Experimental results show that our model achieves state-of-the-art performance on benchmark datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mapping

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TAFM: Topic-Attention FM-encoder for Text Generation 7

TAFM: Topic-Attention FM-encoder for Text Generation

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

作者： He, Dongye Sun, Lijuan Wu, Jingchen Gao, Yutong Wu, Xu Qiu, Lirong Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China Beijing University of Posts and Telecommunications School of Cyberspace Security Bupt Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Bupt Beijing China Bupt Beijing China Beijing Jiaotong University Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Library Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

With the development and application of deep learning, the research of text generation has made great progress, especially in the fields of machine translation, dialogue generation, etc. However, with the application of text generation model in practical engineering, the semantic control of text generation becomes an important problem to be solved urgently. At present, text generation has some good effects in emotional control and temporal control, but it has not achieved good results in semantic control of keywords. The key problem is how to use the keywords semantic information encoding and how to use the keywords semantic information in the text generation process. In view of the above problems, a self encoding and decoding framework integrating factorization machine and topic-weighted attention mechanism is proposed, which is named TAFM. The factorization machine model can focus on refining the combination characteristics between keywords, so as to more comprehensively represent the semantic information of keywords combination. The topic-weighted attention mechanism weights and adds the generated text coding and keyword semantic coding. Adjusting the weight can be vividly understood as adjusting the control intensity of semantic information. Thus, it is more conducive to the expression of topic information. The experimental results show that our method obtains a better BLEU score. In addition, our method can generate coherent sentences containing keywords through manual evaluation. © 2022 IEEE.

关键词： Semantics

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Dynamic Music emotion recognition based on CNN-BiLSTM

Dynamic Music emotion recognition based on CNN-BiLSTM

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Pengfei Du Xiaoyong Li Yali Gao Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education

ISBN: (数字)9781728143231

ISBN: (纸本)9781728143248

Most of the music information will express the emotional signal, the emotional classification of music can be more convenient for the organization and retrieval of music, is the use of music for psychological intervention and physiological adjustment of the premise. In this paper, the dynamic emotion recognition of music, using the VA model, a song to produce a set of VA value. This paper first elaborates the relevant background and emotion classification model of music emotion recognition, and explain the representations of emotion, designs the overall framework of dynamic music emotion recognition, and adopts the convolution neural network and the BiLSTM fusion way to carry on the music dynamic VA And then compared with other relevant methods of identification.

关键词： Music Emotion recognition Feature extraction Logic gates Multiple signal classification Psychology Task analysis

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Research on Staged Practice Teaching System Based on Systems Engineering

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计算机教育 2021年第12期 51-55页

作者： Wanjiang Han Jincui Yang Tao Qi Pengfei Sun Xin Jin Jinpeng Chen Yongping Zhang School of Computer Science(National Pilot Software Engineering School)Beijing University of Posts and Telecommunication Key Laboratory of Trustworthy Distributed Computing and Service(BUPT)Ministry of EducationBeijing100876China

The practical teaching management system is an important factor to ensure the quality of *** training of systematic thinking needs a systematic teaching system,and a systematic process is a gradual process and a standardized *** paper discusses the process of establishing a standardized practice teaching system step by step through a progressive *** the same time,taking test practice as an example,it describes how the practice teaching system enables students to gradually adapt to and benefit from the management of the *** project practice,students not only enhance their practical ability,but also learn the standardized practice *** skills can help students adapt to the work pace of the enterprise faster,and make students’practical ability meet the requirements of the enterprise.

关键词： Practice Teaching System Test Practice Staged Practical Process Standardization System Practice Base

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Employ Multimodal Machine Learning for Content Quality Analysis

Employ Multimodal Machine Learning for Content Quality Analy...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Pengfei Du Xiaoyong Li Yali Gao Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

User-generated content varies drastically, and todays mainstream media sites have a lot of information presented in graphic form. So the task of identifying high-quality content becomes increasingly important, and it can improve overall reading time and ctr(click-through rate estimates). Traditional quality assessment method based only on single modes, for example in image quality assessment area it can divide into IQA method and NR-IQA method, and in text quality assessment area we use different indicators such as readability and smoothness, but there are some limitations in analyzing quality from single mode, many information will lose by single modal analysis. In this paper we propose a multimodal quality recognition approach. We use Transformers-XL which is more efficient and accurate for text feature extract, and use inceptionv3 for the original image feature extract, in order to solve the problem of multi-graph fusion we use NeXtVLAD for graph fusion. Then we use siamese network to score the content quality, and the rank loss is used as the optimization objective. We crawled many data from different website such as WeiboTwitter to constitute the validation data set. Compare with other method such as single modal method and other multimodal deep learning method, our approach got a state of art result.

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Review of Semi-Structured Document Information Extraction Techniques Based on Deep Learning

Review of Semi-Structured Document Information Extraction Te...

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Machine Learning, Cloud computing and Intelligent Mining (MLCCIM), International Conference on

作者： Yangchun Li Wei Jiang Shouyou Song Chinese Academy of Cybersapce Studies School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Cybersapce Studies Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Chinese Academy of Cybersapce Studies Ministry of Education Beijing University of Posts and Telecommunications Beijing China

With the advent of global digital transformation, using an intelligent method based on deep learning to extract crucial information from semi-structured documents, as represented by various types of receipts and invoices, has emerged as an imperative measure to ensure business stability, data security, and improved work efficiency. This paper provides a detailed review on deep learning-based techniques for information extraction, with systematic introduction, hierarchical analysis, method comparison, and summary with expectations for future development. The review begins with a comprehensive explication of the defining characteristics of semi-structured documents, along with a detailed introduction to the research background, application areas, and technical challenges related to information extraction from semi-structured documents. Then the review extends to an overview of two developmental stages, i.e. the shift from traditional information extraction to deep learning-based information extraction, followed by discussion about technical architecture and method classification, which elaborates on key technologies in terms of typical datasets, detection and recognition, and information reduction. Lastly, paper summarizes the prospects and development in the field. Future research will focus on strengthening algorithm universal and lightweight, as well as improving information protection capabilities and the diversity of datasets.

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

With the rapid development of cloud computing technology,the traffic of cloud platform also increases *** of these traffic adopt encryption technology,the original traffic classification method is no longer *** this paper,we propose an efficient encrypted traffic classification method based on pruning convolution neural network:*** 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 *** 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 *** 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： encrypted traffic classification convolutional neural network pruning end-to-end

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DCC-Find: DNS Covert Channel Detection by Features Concatenation-Based LSTM

DCC-Find: DNS Covert Channel Detection by Features Concatena...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Dongxu Han Pu Dong Ning Li Xiang Cui Jiawen Diao Qing Wang Dan Du Yuling Liu Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (Beijing University of Posts and Telecommunications) Beijing China

DNS (Domain Name System) plays an important role in network communication and it is rarely blocked by firewalls and intrusion detection systems (IDS). It is a suitable way for attackers to build DCC (DNS Covert Channel), which is used for data exfiltration. In recent years, some DCC detection methods have been proposed based on deep learning and there is no need for manual feature extraction. However, some expert knowledge is helpful to express the DNS characteristic. In this paper, we propose a FC-LSTM (Features Concatenation-based LSTM) model to detect DCC. The statistical features are concatenated with the output features of the LSTM model. This method makes the expression of DNS domain names more abundant. The experimental results have shown that the DCC traffic can be identified from normal traffic via this model, and the recognition rate is significantly improved compared with the traditional LSTM model and CNN model. In addition, we implement multi-classification in terms of the DCC tools (some of them are used in APT32). We also add generalization DNS packets (simulating APT34 traffic using DCC for stealing and attacking) to verify the robustness of our model. The FC-LSTM model has a good detection performance as well.

关键词： Deep learning Privacy Firewalls (computing) Time series analysis Intrusion detection Manuals Feature extraction

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