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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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Weapon target assignment strategy for multi-missile cooperative guidance with auction algorithm 40

Weapon target assignment strategy for multi-missile cooperat...

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40th Chinese Control Conference, CCC 2021

作者： Li, Qingze Dong, Xiwang Li, Qingdong Yu, Jianglong Ren, Zhang Beihang University School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beijing100191 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing University Chongqing400044 China

ISBN: (纸本)9789881563804

This paper presents a weapon target assignment method to reduce maneuverability overload through redistribution for multi-missile cooperative attack. Firstly, a dynamic constrained model of multi-missile cooperative attack on multi-target is established. Secondly, in order to evaluate the missile's cooperative attack performance better, a cooperative attack probability matrix considering the cooperative attack efficiency is constructed by taking the time-to-go and the line-of-sight rate as indexes. Three kinds of maneuvering overload results are compared to verify the effectiveness of redistribution, including unassigned process, primary target assignment and redistribution at the beginning of mid-to-terminal guidance handover. Then, an auction algorithm is used to solve the target assignment problem under multiple constraints. Finally, the simulation results show that the proposed algorithm is much less time-consuming than the existing intelligent optimization algorithms and meets the real-time requirement. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Combinatorial optimization

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An Uncovered Neurons Information-Based Fuzzing Method for DNN

An Uncovered Neurons Information-Based Fuzzing Method for DN...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing Software

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Compromising Rechargeable Sensor Networks in Marine Environment

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IEEE Transactions on Mobile computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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Decode the Dark Side of the Language: Applications of LLMs in the Dark Web

Decode the Dark Side of the Language: Applications of LLMs i...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Hongfan Chen Yigang Diao Hui Xiang Yixuan Huo Xiaqing Xie Jiapeng Zhao Xuebin Wang Yanwei Sun Jinqiao Shi School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Cyberspace Administration of China Data and Technology Support Center Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Previous studies have discovered that Transformers pre-trained on large scale datasets have a great advantage over traditional methods and can achieve human-level performance in many NLP tasks. Various encoder-only models like BERT and RoBERTa are used in the researches of the language in the Dark Web and most of them have achieved state-of-the-art performance. However, decoder-only models are not frequently used as they were considered to be unsuitable for NLU tasks until recent advancements in large language models. In this paper, we discuss the application of large scale decoder-only models in NLP tasks of real-world Dark Web research. We explore the capabilities of these models with the most realistic datasets in this specific domain. Surprisingly, results show that decoder-only models can achieve similar or better performance compared with state-of-the-art encoder-only model in some cases and show their own unique advantages in such field. We also discuss the limitations of these models and the challenges associated with their application in such field. Our study unveils the potential of decoder-only models in NLP tasks of real-world Dark Web research.

关键词： Dark Web Large language models Text categorization Zero shot learning Cyberspace Data science Transformers Natural language processing Data models Encoding

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STAR-RIS Enabled ISAC systems With RSMA: Joint Rate Splitting and Beamforming Optimization

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Yuan Zhang, Ruichen Jiang, Ruihong Zhu, Yongdong Hu, Huimin Ni, Qiang Fei, Zesong Niyato, Dusit Zhejiang Laboratory Research Center for Space Computing System Hangzhou311100 China Nanyang Technological University College of Computing and Data Science Singapore 639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Cambridge Mechatronics Ltd CambridgeCB1 2PH United Kingdom Xi’an University of Posts and Telecommunications School of Communications and Information Engineering School of Artificial Intelligence Xi’an710121 China Lancaster University School of Computing and Communications LancasterLA1 4WA United Kingdom Beijing Institute of Technology School of Information and Electronics Beijing100081 China

This paper delves into an integrated sensing and communication (ISAC) system bolstered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Within this system, a base station (BS) is equipped with communication and radar capabilities, enabling it to communicate with ground terminals (GTs) and concurrently probe for echo signals from a target of interest. Moreover, to manage interference and improve communication quality, the rate splitting multiple access (RSMA) scheme is incorporated into the system. The signal-to-interference-plus-noise ratio (SINR) of the received sensing echo signals is a measure of sensing performance. We formulate a joint optimization problem of common rates, transmit beamforming at the BS, and passive beamforming vectors of the STAR-RIS. The objective is to maximize sensing SINR while guaranteeing the communication rate requirements for each GT. We present an iterative algorithm to address the non-convex problem by invoking Dinkelbach’s transform, semidefinite relaxation (SDR), majorization-minimization, and sequential rank-one constraint relaxation (SROCR) theories. Simulation results manifest that the performance of the studied ISAC network enhanced by the STAR-RIS and RSMA surpasses other benchmarks considerably. The results evidently indicate the superior performance improvement of the ISAC system with the proposed RSMA-based transmission strategy design and the dynamic optimization of both transmission and reflection beamforming at STAR-RIS. © 2015 IEEE.

关键词： Signal to noise ratio

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Energy and Latency-Aware Resource Management for UAV-Assisted Mobile Edge computing Against Jamming

Energy and Latency-Aware Resource Management for UAV-Assiste...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ziling Shao Helin Yang Liang Xiao Wei Su Zehui Xiong Institute of Artificial Intelligence Xiamen University Xiamen China School of Informatics Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University Xiamen China Pillar of Information System Technology & Design Singapore University of Technology and Design Singapore

Unmanned aerial vehicles (UAVs) have been increasingly employed as aerial servers in mobile edge computing (MEC) systems, providing essential computing, communication, and storage services for edge users. This UAV-assisted MEC paradigm shows great promise in enhancing both computing and communication performances. However, the presence of malicious jammers poses significant challenges to the system's reliability and efficiency. In this study, we explore the resource management problem in a multi-UAV-assisted MEC scenario under the influence of multiple malicious jammers. To mitigate the impact of jamming attacks, we propose a resource management approach with the primary objective of minimizing system energy consumption and latency while adhering to UAV energy constraints. Due to the dynamic and time-varying nature of the communication environment, we present a deep reinforcement learning (DRL)-based algorithm that dynamically adjusts the CPU frequency and communication bandwidth of the UAV to optimize the system performance even under jamming attacks. Through simulations, we demonstrate the effectiveness of the proposed algorithm in significantly reducing the overall system latency (both computational and communication latency) as well as minimizing energy consumption.

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Distributed Online Generalized Nash Equilibrium Tracking for Prosumer Energy Trading Games

Distributed Online Generalized Nash Equilibrium Tracking for...

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American Control Conference (ACC)

作者： Yongkai Xie Zhaojian Wang John Z.F. Pang Bo Yang Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China Agency for Science Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Singapore Republic of Singapore

With the proliferation of distributed generations, traditional passive consumers in distribution networks are evolving into "prosumers", which can both produce and consume energy. Energy trading with the main grid or between prosumers is inevitable if the energy surplus and shortage exist. To this end, this paper investigates the peer-to-peer (P2P) energy trading market, which is formulated as a generalized Nash game. We first prove the existence and uniqueness of the generalized Nash equilibrium (GNE). Then, an distributed online algorithm is proposed to track the GNE in the time-varying environment. Its regret is proved to be bounded by a sublinear function of learning time, which indicates that the online algorithm has an acceptable accuracy in practice. Finally, numerical results with six microgrids validate the performance of the algorithm.

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Auto-MatRegressor材料性能自动预测器:解放材料机器学习"调参师"

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Science Bulletin 2023年第12期68卷 1259-1270,M0004页

作者：刘悦王双燕杨正伟 Maxim Avdeev 施思齐 School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Zhejiang Laboratory Hangzhou 311100China Australian Nuclear Science and Technology Organisation Sydney 2232Australia School of Chemistry The University of SydneySydney 2006Australia

机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习"调参师",本文研发了基于元学习的材... 详细信息

机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习"调参师",本文研发了基于元学习的材料性能自动预测器,采集了60份文献公开数据集与60份标准数据集,基于此训练18种常用回归算法并获得其预测性能,定义与计算了27个刻画数据集特点的元特征,以此构建了一份蕴含建模经验的元数据集;同时,创建了表征数据集所属材料类型的类别树,将其嵌入基于距离的元学习算法,进一步耦合贝叶斯优化算法,实现领域知识和元数据协同驱动下的自动算法推荐和模型参数确定,实验结果表明,材料科学家仅需为新材料性能预测任务提供数据集,便可利用该预测器高效地构建具有与文献报道相当或更高预测精度的机器学习模型.

关键词：机器学习贝叶斯优化算法预测器材料科学家元数据参数寻优预测性能材料性能

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Meet-in-the-Middle Statistical Fault Analysis of the PRESENT lightweight Cryptosystem

Meet-in-the-Middle Statistical Fault Analysis of the PRESENT...

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作者： Li, Wei Zhu, Xiao-Ming Gu, Da-Wu Li, Jia-Yao Cai, Tian-Pei School of Computer Science and Technology Donghua University Shanghai201620 China Department of Computer and Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Scalable Computing and System Shanghai200240 China Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Shanghai200240 China

The lightweight block cipher PRESENT was presented at the International Conference on Cryptographic Hardware and Embedded systems (CHES) in 2007, and it has become a standard of ISO/IEC-29192-2 for lightweight cryptosystems since 2012. Within the environment of the Internet of Things, this cipher is applicable to Radio Frequency Identification, network sensors, and smart cards. Its secret key can be either 80 or 128 bits long, while the block is 128 bits long. Since the publication of the PRESENT, numerous forms of cryptanalysis have been developed in order to evaluate its level of security. Some examples of these attacks include the differential attack, the linear attack, the integral attack, the algebraic attack, the fault attack, the side-cube attack, and the Biclique attack. All of these cryptanalyses are on the basis of the assumption that an attack using a chosen plaintext or an attack using a known plaintext would occur. In order to carry out a chosen-plaintext attack, an attacker must have the ciphertexts along with the plaintexts that go along with them, whereas a known-plaintext attack requires a significant quantity of plaintexts and ciphertexts. The ciphertext-only attack is distinct from the chosen-plaintext attack and the known-plaintext attack, which require the ciphertext only in order to be successful. In this sense, the ciphertext-only attack is relevant in a range of scenarios. There is no research that has been released yet regarding PRESENT'S resilience to the ciphertext-only attack. This study presents a novel meet-in-the-middle statistical fault analysis using the approach of statistical analysis and meet-in-the-middle attack of PRESENT. This analysis is able to decrypt the 80-bit and 128-bit secret keys of PRESENT with a series of new distinguishers that include the Pearson correlation coefficient-Hamming weight, the Kullback Leibler divergence- Hamming weight, and the Jaccard similarity coefficient-Hamming weight-maximum likelihood estimatio

关键词： Side channel attack

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