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9th IEEE Smart World Congress, SWC 2023

作者： Zhu, Jie Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Panneerselvam, John Jiangsu University School of Computer Science and Telecommunication Engineering China Jiangsu University Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace China University of Leicester School of Computing and Mathematical Sciences United Kingdom Jiangsu University of Science and Technology School of Ocean China

ISBN: (纸本)9798350319804

Twitter has become a crucial social networking platform for users to receive and exchange information about current events. Identifying the most influential users in social networks is pivotal for the efficient propagation of information. However, traditional influencer identification models often fall short of assessing users' influence comprehensively and neglect overlapping influence, which greatly restrains their efficiency and accuracy in identifying key users. To address such issues, we propose a method for identifying key users based on correlation and minimum distance (CM-UDM). This method can evaluate users' influence based on their individual, global, local, and propagation path characteristics in social networks. Additionally, we propose a key user decision graph in order to address the problem of overlapping users' influence. We have conducted experiments using one classical dataset and three real Twitter datasets, where the performance of our proposed method in identifying highly influential users is demonstrated. © 2023 IEEE.

关键词： Influence maximization Influence overlap Twitter

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An Adaptive Community Detection Algorithm based on Density Peaks 9

An Adaptive Community Detection Algorithm based on Density P...

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9th IEEE Smart World Congress, SWC 2023

作者： Fan, Xiang Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Yuan, Bo Jiangsu University School of Computer Science and Telecommunication Engineering China Jiangsu University Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace China University of Leicester School of Computing and Mathematical Sciences United Kingdom Jiangsu University of Science and Technology School of Ocean China

ISBN: (纸本)9798350319804

Community detection is an important task for analyzing the topological function and structure of a network, as well as for various application scenarios such as advertising, marketing, and influence maximization. Label propagation is a widely used community detection algorithm for complex networks due to its simplicity and fast computation. However, this algorithm has several limitations in terms of stability and the quality of discovered communities. To address these shortcomings, we propose a novel community detection algorithm based on density peaks (ACDDP) in this paper. Firstly, we improve the local density and minimum distance of nodes by using an adaptive selection of community center nodes. Secondly, we specify the node update order to enhance the robustness and performance of the algorithm. The experimental results on real networks demonstrate that our proposed algorithm achieves higher accuracy in community division while ensuring robustness compared to other popular algorithms. © 2023 IEEE.

关键词： Adaptive Selection Community detection Density Peaks

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Effects of Crystallinity and Defects of Layered Carbon Materials on Potassium Storage:A Review and Prediction

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Electrochemical Energy Reviews 2022年第2期5卷 401-433页

作者： Xiaoxu Liu Tianyi Ji Hai Guo Hui Wang Junqi Li Hui Liu Zexiang Shen Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials School of Material Science and EngineeringShaanxi University of Science and TechnologyXi’an 710021ShaanxiChina Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore College of Computer Science and Technology Dalian Minzu UniversityDalian 116650LiaoningChina

Layered carbon materials(LCMs)are composed of basic carbon layer units,such as graphite,soft carbon,hard carbon,and *** they have been widely applied in the anode of potassium-ion batteries,the potassium storage mechanisms and performances of various LCMs are isolated and difficult to relate to each *** importantly,there is a lack of a systematic understanding of the correlation between the basic microstructural unit(crystallinity and defects)and the potas-sium storage *** this review,we explored the key structural factors affecting the potassium storage in LCMs,namely,the crystallinity and defects of carbon layers,and the key parameters(L_(a),L_(c),d_(002),I_(D)/I_(G))that characterize the crystallinity and defects of different carbon materials were extracted from various databases and literature sources.A structure–property database of LCMs was thus built,and the effects of these key structural parameters on the potassium storage properties,including the capacity,the rate and the working voltage plateau,were systematically *** on the structure–prop-erty database analysis and the guidance of thermodynamics and kinetics,a relationship between various LCMs and potas-sium storage properties was ***,with the help of machine learning,the key structural parameters of layered carbon anodes were used for the first time to predict the potassium storage performance so that the large amount of research data in the database could more effectively guide the scientific research and engineering application of LCMs in the future.

关键词： Layered carbon materials Crystallinity and defects Potassium storage properties Machine learning

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Maths Anxiety and cognitive state monitoring for neuroadaptive learning systems using electroencephalography 2

Maths Anxiety and cognitive state monitoring for neuroadapti...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Armani, Federica Daly, Ian Vernitski, Alexei Gillmeister, Helge Scherer, Reinhold School of Computer Science and Electronic Engineering University of Essex Brain-Computer Interfaces and Neural Engineering Laboratory ColchesterCO43SQ United Kingdom University of Essex Department of Mathematical Sciences ColchesterCO43SQ United Kingdom University of Essex Centre for Brain Science Department of Psychology ColchesterCO43SQ United Kingdom

ISBN: (纸本)9798350300802

mathematical competence is important to acquire for everyday and professional purposes, but often represents a considerable hurdle for students, who may associate it with unpleasant experiences. Our goal is to use neuroscience and neural engineering to support students to improve their mathematical understanding. More specifically, we are interested in the development of a non-invasive electroencephalogram (EEG)-based neuroadaptive Brain-computer Interface (BCI) learning environment that optimizes learning outcomes by adapting the learning content provided according to the cognitive load of the learner. In this paper, we investigate what cognitive states occur when students with and without Math Anxiety learn to solve a math problem presented in the form of a novel computer puzzle. Results of an offline analysis of data recorded from 10 study participants suggest that different cognitive states occur, each with specific features that a BCI could potentially detect. © 2023 IEEE.

关键词： Brain computer Interfaces (BCI) electroencephalogram (EEG) Math Anxiety neuroadaptive technologies

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Red VCSEL Array for Optical Parallel-Interconnected Links with Ultra-Low Energy Consumption

Red VCSEL Array for Optical Parallel-Interconnected Links wi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Nawal Almaymoni Omar Alkhazragi Fabian Finkbeiner Tien Khee Ng Boon S. Ooi Photonics Laboratory Electrical and Computer Engineering Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Materials Science and Engineering Division of Physical Science and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

We demonstrated a high data rate of 4.7 Gb/s based on a single 650-nm vertical-cavity surface-emitting laser with 2-pJ/bit energy consumption, potentially enabling Tb/s parallel interconnects based on a $14\times 16$... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical fibers Energy consumption Optical interconnections Surface waves Electro-optic effects Light emitting diodes Arrays Vertical cavity surface emitting lasers Optical arrays Optical surface waves

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Robust Watermarking Based on Multi-layer Watermark Feature Fusion

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IEEE Transactions on Multimedia 2025年

作者： Wu, Shaowu Lu, Wei Luo, Xiangyang Sun Yat-Sen University School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450002 China

The purpose of robust image watermarking is to embed a watermark into a carrier image in an invisible form and extract the watermark successfully even under noise interference conditions to achieve copyright confirmation and traceability. Although watermarking methods based on deep learning can improve the robustness by adding a noise simulation layer, few theoretical analyses of the codec structure have been conducted. Theoretical explainability is the theoretical basis for developing a network architecture, which plays a guiding role in network development. On the basis of the interpretability of convolutional networks, this paper analyzes the mathematical process of embedding and extracting watermarks in codecs and proposes a novel watermarking framework based on multi-layer watermark feature fusion. Specifically, the encoder can be a convolutional network structure of arbitrary depth, whereas the decoder needs only to adopt its corresponding deconvolution structure. To improve the quality and robustness of the generated watermarked image, the watermark is associated with an arbitrary layer feature space in the decoder. In the decoder, the network quickly converges to each original encoding feature space through the deconvolution structure, thus decoupling the watermark features. Finally, the watermark is extracted via the automatic fusion of multi-layer watermark features. The experimental results show that the proposed method is suitable for few-shot learning, and its invisibility, robustness and generalization performance on multiple datasets are significantly better than those of other advanced methods. © 2025 IEEE.

关键词： Decoding

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A secure spectrum auction scheme without the trusted party based on the smart contract

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Digital Communications and Networks 2021年第2期7卷 223-234页

作者： Jiaqi Wang Ning Lu Qingfeng Cheng Lu Zhou Wenbo Shi School of Computer Science and Engineerings Northeastern University Shenyang 110004China School of Computer Science and Technology Xidian UniversityXi'anChina Strategic Support Force Information Engineering University Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Division of Computer Science University ofAizuAizu-WakamatsuJapan School of Computer and Communication Engineering Nordieastem UniversityQinhuangdao066004China

With the development of communication 5G networks and technologies,spectrum resources are increasingly *** scarcity of the spectrum resource makes market-driven spectrum auction become an important means of spectrum allocation,and due to the complexity of the network environment,the security of spectrum auctions can not be *** existing secure spectrum auction schemes introduce a semi-honest agent to complete spectrum ***,the hypothetical semi-honest model does not guarantee the security of spectnim auction in the actual application scenario,which may lead to potential security threats:the agent may reveal the privacy of bidders,agent or auctioneer may collude with the bidder to manipulate the spectrum auction,and so *** this paper,a secure spectrum auction scheme without a trusted party is proposed based on the smart contract technology,and the smart contract written into the blockchain replaces the traditional semi-honest agent to cooperate with the auctioneer server to complete the *** order to ensure the security of our scheme,a secure spectrum auction protocol is designed,in which the Software Guard Extensions(SGX)technology and Paillier cryptosystem are used to protect the privacy of *** verification is provided in our protocol by using extensive Pedersen commitment,which prevents the auctioneer server and the bidder from colluding with each other and verifies group bid sum ***,the security analysis is given to propose several types of attacks that can be ***,theoretical analysis and simulation experiments of our protocol are also provided.

关键词： 5g Secure spectrum auction Bidders'bids'privacy Smart contract Public verification

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Over-the-RIS Modulation: A Reconfigurable Intelligent Surface Assisted Modulation Design 8

Over-the-RIS Modulation: A Reconfigurable Intelligent Surfac...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Huang, Yiqian Zammit, Saviour Yang, Ping Xiao, Yue Zhang, Bo Chen, Jienan Xiang, Wei University of Electronic Science and Technology of China National Key Laboratory of Sciencec and Technology on Communications Sichuan China University of Malta Department of Communications and Computer Engineering Msida Malta 32806 Troops Beijing China School of Computing Engineering and Mathematical Sciences La Trobe University Melbourne Australia

ISBN: (纸本)9798350305838

This paper proposes an improved over the air modulation (OTAM) scheme by using the powerful reconfigurable intelligent surfaces (RISs), namely over-the-RIS modulation (ORM). In the design framework, we reconstruct multipath channels by employing several RISs to increase the attenuation difference between multiple paths. Compared with conventional OTAM, ORM can be used in more generic fading scenarios, e.g., channels with non-ideal attenuation properties, and thus extends the application of OTAM. Based on the Bayesian criterion, we propose an optimal likelihood detector for the ORM system and derive a closed-form expression of the bit error rate (BER). Furthermore, we investigate the phase optimization of the RISs to improve its BER and deduce its closed-form solutions. Simulation results are presented to show that the proposed ORM system can achieve a better BER performance and higher transmission rate than the conventional OTAM systems, especially for environments with severe shadow fading. © 2023 IEEE.

关键词： Bit error rate

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VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model for C/C++ Code

VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model ...

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Zhe Xiong Weiyu Dong School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Recently, deep learning techniques have received widespread attention in code vulnerability and vulnerability detection. However, there are some problems with current vulnerability detection techniques, firstly, because of the non-homogeneity of vulnerability categories and quantities, there are omission false positives and overfitting problems for vulnerabilities with a small amount of data. Second, the existing detection methods have limited ability to characterize the code, there is insufficient depth of characterization, does not contain all of its vulnerability characteristics or is interspersed with useless information. Finally, the current research does not use the powerful feature mining capability of pre-trained models and does not employ model fusion to increase the effectiveness of model vulnerability detection. To solve the above problems, this paper proposes a new deep learning based pre-trained model for code vulnerability detection tool VulD-CodeBERT. By fine-tuning CodeBERT, a code adaptation extension for pre-trained models developed by Microsoft, the CodeBERT model can solve the problem of not being able to learn the vulnerability patterns due to insufficient data, thus resulting in missed false alarms. The sequence output from the CodeBERT model are added to a BiLSTM network with an attention mechanism to mine the local key features of the vulnerabilities combined with the global features of the pooler output from the CodeBERT model to deeply mine the dependencies between the vulnerability statements and derive the probability of the existence of the vulnerability. For vulnerability types that are difficult to uncover captured features or the number of samples is not enough, a polynomial plain Bayesian classifier is used for detection, and finally the two models are combined to deeply mine the vulnerability features, thus solving the problem of difficult and small samples, and reducing the leakage rate and false alarm rate. Experimental results sh

关键词： Deep learning Adaptation models Codes Attention mechanisms Computational modeling Feature extraction Polynomials

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