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Recent development on online public opinion communication and early warning technologies: Survey

EXPERT SYSTEMS WITH APPLICATIONS 2025年 284卷

作者： Wu, Wei Yang, Yawen Qiao, Tianlu Peng, Haipeng Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing Peoples R China

The rapid development of the Internet and communication technologies has led to the emergence of online public opinion, which plays a crucial role in disseminating information and guiding the public. However, due to its large volume of data and fast-paced changes, automated monitoring of online public opinion is challenging. Currently, advancements in artificial intelligence (AI) technology provides new solutions for detecting and early warning of online public opinion. This paper reviews the literature in the field of online public opinion research over the years. Firstly, it systematically elucidates the formation mechanism and dissemination model of online public opinion. Secondly, it focuses on the analysis of public opinion monitoring technology in the era of artificial intelligence, clarifying the research branches of online public opinion early warning technology. Finally, it identifies the shortcomings in current research on online public opinion and proposes directions for future research.

关键词： Online public opinion Public opinion communication Public opinion analyzing Public opinion warning Artificial intelligence

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CLAD: A Deep Learning Framework for Continually Learning in Anomaly Detection 2022

CLAD: A Deep Learning Framework for Continually Learning in ...

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5th International Conference on Software Engineering and Information Management, ICSIM 2022

作者： Cao, Yulu Gan, Honglu State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450395519

The rapid development and frequent revolutions in information technology (like Edge Computing, Wireless Sensor Network) highlight the significance of Internet of Things. Nowadays, a variety of infrastructures from diverse fields are limited by external and internal environmental factors. In actual operation, these factors may cause serious anomalies and aggravate the burden of facilities' maintenance. With a rise in the number and running time, the performance of these facilities would be unstable and complex. This paper proposes a deep learning framework (CLAD) to do adaptive anomaly detection. It implements dynamic anomaly thresholding based on the prediction of incremental Long Short-Term Memory. This framework employs a replay buffer to solve the decline of detection accuracy. With this framework, models can keep perfect detection accuracy under a quite high load (time and number). This framework is valuable for further research of adaptive anomaly detection. © 2022 ACM.

关键词： Anomaly detection

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Tripartite Svetlichny test with measurement dependence

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FRONTIERS IN PHYSICS 2024年 12卷

作者： Li, Run-Ze Li, Dan-Dan Wu, Sheng-Yao Qin, Su-Juan Gao, Fei Wen, Qiao-Yan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing Peoples R China

The Bell test, as an important method for detecting nonlocality, is widely used in device-independent quantum information processing tasks. The security of these tasks is based on an assumption called measurement independence. Since this assumption is difficult to be guaranteed in practical Bell tests, it is meaningful to consider the effect of reduced measurement independence (i.e., measurement dependence) on Bell tests. Some research studies have shown that nonlocality can be detected even if measurement dependence exists. However, the relevant results are all based on bipartite Bell tests, and the results for multipartite Bell tests are still missing. In this paper, we explore this problem in the tripartite Svetlichny test. By considering flexible lower and upper bounds on the degree of measurement dependence, we obtain the relation among measurement dependence, guessing probability, and the maximal value of Svetlichny inequality. Our results reveal the case in which genuine nonlocality is nonexistent;at this point, the outcomes of the Bell test cannot be applied in device-independent quantum information processing tasks.

关键词： Bell test Bell nonlocality measurement dependence Svetlichny inequality device-independent quantum information task

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Design Framework of Unsourced Multiple Access for 6G Massive IoT

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China Communications 2024年第1期21卷 1-12页

作者： Chunlin Yan Siying Lyu Sen Wang Yuhong Huang Xiaodong Xu China Mobile Communication Research Institute Beijing 100053China ZGC Institute of Ubiquitous-X Innovation and Applications Beijing 100080China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions.A rather complete design of unsourced multiple access is proposed in which two key parts:a compressed sensing module for active user detection,and a sparse interleaver-division multiple access(SIDMA)module are simulated side by side on a same platform at balanced signal to noise ratio(SNR)operating *** a proper combination of compressed sensing matrix,a convolutional encoder,receiver algorithms,the simulated performance results appear superior to the state-of-the-art benchmark,yet with relatively less complicated processing.

关键词： channel coding compressed sensing massive Internet-of-Things(IoT) sparse interleaverdivision multiple access(SIDMA) the sixth generation(6G)mobile communications unsourced multiple access

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High-capacity medical information hiding scheme with P-tensor product secret image sharing for wireless body area networks

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DIGITAL SIGNAL PROCESSING 2024年 146卷

作者： Wang, Lanlan Peng, Haipeng Li, Lixiang Bao, Shuang Liang, Junying Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Natl Engn Lab Disaster Backup & Recovery Beijing 100876 Peoples R China

Given the escalating demand for information transmission in the resource-restricted wireless body area networks (WBANs), securing medical data and minimizing energy consumption are paramount. We present an innovative information hiding scheme that employs P-tensor product secret image sharing (PTP-SIS) and multiple information-hiders for WBANs. Considering the remarkable compression performance demonstrated by compressed sensing (CS), as well as its link to Shamir's (k, n) secret sharing method, using CS rather than traditional secret sharing techniques for image processing purposes could potentially help alleviate transmission burden of sensor nodes. However, utilizing CS to process high-dimensional signals poses a significant challenge due to the large dimensionality of the measurement matrix. The P-tensor product (PTP) is a tool that offers outstanding universality and can overcome the dimension limitations of traditional matrix multiplication, it is applied in the secret sharing stage. Moreover, applying the ideas of secret sharing leads to less stress on an individual device as well as lower communication spending, and multiple hiders have the ability to independently embed additional secrets into shadow images for efficient information backup. Theoretical analysis and experiments validate that the proposed scheme ensures information security and transmission efficiency while achieving superior image recovery performance and higher information embedding rates compared to other approaches.

关键词： Wireless body area networks (WBANs) P-tensor product (PTP) Secret sharing Compressed sensing (CS) Information hiding

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Decentralized Cooperative Caching and Offloading for Virtual Reality Task Based on GAN-Powered Multi-Agent Reinforcement Learning

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第1期17卷 291-305页

作者： Yang, Yang Feng, Lei Sun, Yao Li, Yangyang Zhou, Fanqin Li, Wenjing Wang, Shangguang Beijing Univ Posts & Telecommun BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland

As a critical and prevalent service in future mobile networks, virtual reality (VR) is latency-sensitive and power-hungry, bringing out the optimization problem of trade-off among power saving, delay, and resource utilization. Content caching and render offloading are deemed as promising solutions to meet the stringent requirements of VR on data transmission speed and end-to-end latency. In this article, we propose a novel distributed computing framework based on multi-agent deep deterministic policy gradient (MADDPG) for joint optimizing terminal-cooperative caching and offloading for VR tasks. Since the individual VR user can hardly reach the optimal actions based on its limited local observed states and samples, MADDPG with centralized training and distributed execution is exploited to solve the above challenge. In addition, the generative adversarial network (GAN) is introduced to obtain experience-enhanced agents in the offline training phase and to achieve an optimal allocation to minimize energy consumption in the online inferring phase. The Nash equilibrium is proven in the case that the distribution of finite real VR data samples is well imitated and complemented by GAN. Numerical results demonstrate that our algorithm has significant superiorities in terms of convergence performance and energy consumption over other benchmarks.

关键词： Task analysis Optimization Servers Resource management Rendering (computer graphics) Generative adversarial networks Energy consumption VR decentralized networks MADDPG GAN caching and offloading enhanced experience

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Satellite Computing: Vision and Challenges

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IEEE INTERNET OF THINGS JOURNAL 2023年第24期10卷 22514-22529页

作者： Wang, Shangguang Li, Qing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China

The space industry experiences a rise in low-Earth-orbit satellite mega-constellations to achieve universal connectivity. At the same time, cloud firms (such as Google, Microsoft, and AWS) also have ambitions for computing in space to offer public cloud services in orbit. Satellite computing, as a new emerging concept, is promising to enable new paradigms in on-orbit autonomy, remote sensing, edge computing, and other areas by empowering satellites with computing resources. However, LEO mega-constellations bring inherent challenges for satellite computing in satellite networking, computing, and others due to the moving core infrastructure, reduced system power budget, and harsh space environment. This article presents vision and challenges for satellite computing based on a brief survey of the very recent literature in the "NewSpace" era and gives a case study of an open research platform on real satellites named Tiansuan constellation. This article aims to call researchers to collaboratively undertake the research of satellite computing and provide some insights for the research community.

关键词： Satellite computing satellite Internet testbed

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DNN Inference Task Reconstruction Algorithm for Edge Fault 14th

DNN Inference Task Reconstruction Algorithm for Edge Fault

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14th International Conference on Computer Engineering and Networks, CENet 2024

作者： Wei, Junjie Wang, Ying Zhang, Manjun Qiu, Xuesong Zhuang, Zhihan Cai, Junru Wu, Ningchao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789819649624

With the rapid development of the Internet of Things and machine learning, deep neural networks have been widely applied and developed in multiple fields. At the same time, thanks to the development of mobile edge computing, the deployment of DNN reasoning tasks sinks from the remote central cloud to the edge side. However, the failure probability of edge computing nodes is much higher than that of central cloud. Therefore, when an edge server fails, how to better reconstruct and resume the execution of DNN inference tasks is a key issue. To address the aforementioned issues, we propose an edge fault oriented DNN inference task reconstruction method. Firstly, we established a problem model for the redeployment of edge fault oriented DNN inference tasks based on cloud edge network environment. The problem model optimizes the reliability and delay weights of the reconstructed DNN inference tasks. Then we designed a meta heuristic method based on simulated annealing algorithm to find the optimal redeployment scheme for DNN inference tasks. Finally, we evaluated the performance of our proposed algorithm through simulation. The results showed that our algorithm outperformed Random, LB, and RMA algorithms by an average of 36.4%, 22.5%, and 21.8%, respectively, in terms of optimization objectives. The above experimental results indicate that the algorithm proposed in this paper can provide an effective redeployment scheme for DNN inference tasks caused by edge faults. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile edge computing

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SM³: Self-supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects

SM<SUP>3</SUP>: Self-supervised Multi-task Modeling with Mul...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wang, Haowen Zhao, Zhen Jin, Zhao Che, Zhengping Qiao, Liang Huang, Yakun Fan, Zhipeng Qiao, Xiuquan Tang, Jian Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Liangq Peoples R China Midea Grp Foshan Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused on supervised approaches, relying on annotated datasets to model articulated objects within limited categories. However, these approaches fall short of effectively addressing the diversity present in the real world. To tackle this issue, we propose a self-supervised interaction perception method, referred to as SM3, which leverages multi-view RGB images captured before and after interaction to model articulated objects, identify the movable parts, and infer the parameters of their rotating joints. By constructing 3D geometries and textures from the captured 2D images, SM3 achieves integrated optimization of movable part and joint parameters during the reconstruction process, obviating the need for annotations. Furthermore, we introduce the MMArt dataset, an extension of PartNet-Mobility, encompassing multi-view and multi-modal data of articulated objects spanning diverse categories. Evaluations demonstrate that SM3 surpasses existing benchmarks across various categories and objects, and its adaptability in real-world scenarios has been thoroughly validated.

关键词： Self-supervised learning

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How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

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IEEE WIRELESS COMMUNICATIONS LETTERS 2023年第4期12卷 649-653页

作者： Sun, Yutong Zhang, Jianhua Yu, Li Zhang, Zhen Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518066 Peoples R China

In view of the propagation environment directly determining the channel properties, the applications can also be solved with the aid of environmental information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, we aim to provide a new view of the environment from the semantic level. This letter defines propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS). The PESS is extracted, which is oriented to the application tasks with concerned channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is investigated and implemented for non-line-of-sight (NLOS) scenarios. Environment features and graph representations are constructed for actions of channel quality evaluation and target scatterer detection with maximum power. It can obtain 0.92 and 0.9 precision, respectively, and save over 87% of the time cost.

关键词： Semantics Task analysis Feature extraction Kernel Support vector machines Structural beams Symbols Propagation environment semantics semantic mapping propagation semantic symbols extraction beam prediction

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