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Maximizing Influence in Temporal Social networks:A Node Feature-Aware Voting Algorithm

computers, Materials & Continua 2023年第12期77卷 3095-3117页

作者： Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of ***,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social *** bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation ***,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes ***,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their *** the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability *** process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based ***,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node ***,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

关键词： Temporal social networks influence maximization voting strategy interactive properties self-similarity

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Integrating Syntactic and Semantic Knowledge in AMR Parsing with Heterogeneous Graph Attention network 48

Integrating Syntactic and Semantic Knowledge in AMR Parsing ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sataer, Yikemaiti Shi, Chuanqi Gao, Miao Fan, Yunlong Li, Bin Gao, Zhiqiang Southeast University School of Computer Science and Engineering Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Meaning Representation (AMR) parsing is the task of translating a sentence to an AMR semantic graph which captures the basic meaning of the sentence, and is empowered by pre-trained Transformer models recently. These models encode the syntactic and semantic knowledge implicitly through self-supervised pre-trained tasks. We argue that encoding the syntactic and semantic knowledge explicitly is beneficial to AMR parsing and can improve data efficiency. Specifically, syntactic dependency and semantic role labeling (SRL) have similar sub-structures with AMR. In this work, we propose a novel linguistic knowledge-enhanced AMR parsing model, which augments the pre-trained Transformer with syntactic dependency and semantic role labeling structures of sentences. By applying a heterogeneous graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experimental results show that our model achieves state-of-the-art performance on different benchmarks, especially in out-of-domain and low-resource scenarios. © 2023 IEEE.

关键词： Syntactics

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Image Compression Algorithm Based on Region of Interest Extraction for Unmanned Aerial Vehicles Communication 8

Image Compression Algorithm Based on Region of Interest Extr...

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8th IEEE information Technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Liang, Yanxia Jia, Tong Liu, Xin Zhang, Huanhuan Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Xi'an Eurasia University College of Information Engineering Xi'an China

ISBN: (纸本)9798331517915

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) have led to their widespread use, but the challenge of limited storage space and bandwidth in emergency scenarios necessitates efficient image compression techniques. In this research, we propose an image compression algorithm tailored for UAV communication, termed ROIE-UC (Region of Interest Extraction for UAV Communication). Firstly, image pixels are clustered into super pixel blocks using the Simple Linear Iterative Clustering (SLIC). Secondly, these super pixels are grouped into regions of interest (ROI) using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The image is then segmented into ROI and non-ROI areas based on these clusters. Lossless compression is applied to the ROI, while lossy compression with a high ratio is used for non-ROI regions. At the receiving end, the image is decompressed and reconstructed. Experimental results show that the ROIE-UC achieves a Peak Signal-to-Noise Ratio (PSNR) of 46.37 dB and a Structural Similarity Index (SSIM) of 99.94% for the ROI part, with compression ratios of 27, 25, and 14 for the whole image. Compared to the JPEG, PSNR improved by up to 28.52% and SSIM by 1.51%, with a compression ratio increase from 14.52 to 27.56. The ROIE-UC also provides up to a 49.44% increase in PSNR over other methods. These results demonstrate that ROIE-UC is an efficient and effective solution for image processing and data compression in UAV applications. © 2025 IEEE.

关键词： cluter image compression PSNR regions of interest SSIM

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Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

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Chinese Physics B 2023年第2期32卷 136-149页

作者：王兴元王哓丽滕琳蒋东华咸永锦 School of Information Science and Technology Dalian Maritime UniversityDalian 116026China Guangxi Key Laboratory of Multi-source Information Mining&Security Guangxi Normal UniversityGuilin 541004China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 511400China School of Cyber Security Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform(DWT)on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics(PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.

关键词： chaotic image encryption compressive sensing meaningful cipher image portable network graphics image encryption algorithm

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EPI-Mamba: State Space Model for Semantic Segmentation from Light Fields

EPI-Mamba: State Space Model for Semantic Segmentation from ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yan Chen, Jianan Wang, Qiong Xu, Jinshan College of Computer Science & Technology Zhejiang University of Technology Hangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Global contextual dependency is of significance for semantic segmentation from light fields. However, previous works mostly exploit attention mechanisms to model spatial context dependency and angular context dependency separately, since a light field capture is very data-intensive. Considering that self-attention is resource-consuming, existing methods only use it to capture spatial contextual dependency for the central view of light fields, and employ resource-efficient attention mechanisms to model angular view dependencies instead. Inspired by the recent success of State Space Sequence model (SSM), we present a novel SSM-based encoder for light field semantic segmentation, which models spatio-angular contextual dependency together. The proposed method is evaluated on the existing light field semantic segmentation datasets, and experimental results show that the proposed method significantly outperforms previous state-of-the-art methods. The code and models used in this work are available in https://***/YanWQ/EPI-Mamba. © 2025 IEEE.

关键词： Light field semantic segmentation Mamba State space model

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A Blackbox Fuzzing Based on Automated State Machine Extraction 8

A Blackbox Fuzzing Based on Automated State Machine Extracti...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Zhang, Zhaowei Yu, Wenjing Wang, Zibin Xiang, Youlin National Key Laboratory of Science and Technology on Information System Security Beijing China School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Shanghai China

ISBN: (纸本)9798350331035

Currently, protocol fuzzing techniques mainly employ two approaches: greybox fuzzing based on mutation and blackbox fuzzing based on generation. Greybox fuzzing techniques use message exchanges between the protocol server and actual clients as seeds, and generate test cases through mutation. Although this approach can provide coverage information of the SUT's code and state space through instrumentation and feedback, its drawback lies in the relatively random mutation strategy, which makes it challenging to validate the SUT's message verification process. This paper addresses this limitation by utilizing artificial intelligence techniques to extract protocol state machines. It aims to overcome the reliance on manual work in blackbox fuzzing based on generation and leverage its advantages to generate more effective fuzzing test cases. The study utilizes Prompt-Learning technology to analyze the semantic information in protocol RFC documents, obtain corresponding intermediate representations, and extract protocol state machines from these representations. Taking the BGP protocol as an experimental subject, the experimental results demonstrate a certain level of accuracy in the obtained protocol state machines and the ability to generate test cases from these state machines, thereby enhancing the automation level of protocol fuzzing. © 2023 IEEE.

关键词： fuzzing network protocols network security protocol testing state machine extraction

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A Low-Rank Matching Attention Based Cross-Modal Feature Fusion Method for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 1177-1189页

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo Xi'An Jiaotong School of Computer Science and Technology Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi'an710049 China Xi'An Jiaotong School of Computer Science and Technology Shaanxi Xi'an710049 China BigKEJoint Innovation Center SHMEEA Shanghai China Xi'An Jiaotong University School of Mathematics and Statistics Xi'an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. © 2024 IEEE.

关键词： Semantics

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Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

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computers, Materials & Continua 2024年第8期80卷 2579-2609页

作者： Yangrong Chen June Li Yu Xia Ruiwen Zhang Lingling Li Xiaoyu Li Lin Ge School of Computer Science Zhengzhou University of AeronauticsZhengzhou450046China Henan Xin’an Century Technology Co. Ltd.Henan Data Security Engineering Technology Research CenterZhengzhou450046China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control *** the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication *** grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for ***,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for ***,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security ***,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal *** index system contains 18 indicators in 3 ***,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and *** higher the level,the greater the security ***,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by *** results show that calculated security risk level based on the proposed assessment method are consistent with actual ***,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

关键词： Smart grid intelligent electronic device security assessment abnormal behaviors network traffic running states

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Intermittent Artificial Noise Assisted Covert Cooperative Backscatter Communication with Residual Hardware Impairments

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2024年

作者： Gao, Zhangrui Li, Huan Ye, Yinghui Lu, Guangyue Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security China

Existing works on artificial noise (AN) assisted covert backscatter communication (BC) assumed a jammer continuously transmitting AN, leading to the reduction of the reliability of the legitimate link. Additionally, the unavoidable residual hardware impairments (RHIs) in transceivers have been largely ignored particularly for AN assisted covert BC. Motivated by the above two observations, in this paper, we investigate an intermittent AN assisted covert cooperative backscatter communication (CoBC) system under RHIs. To assess its covertness and reliability, we derive approximate expressions for the detection error probability (DEP) of an eavesdropper and the system outage probability (SOP) under a high transmit signal-to-noise ratio (SNR) region, respectively. To obtain a satisfactory covertness of system, we devise a problem to jointly optimize the AN transmission power and the eavesdropping detection threshold to maximize the minimum DEP while meeting a maximum tolerance system outage probability, and propose an iterative algorithm based on block coordinated descent (BCD) to solve it. Numerical simulations confirm the accuracy of derived results and the valid of the proposed algorithm, and reveal that the intermittent AN achieves a better system reliability than the continuous one when the system requirement for covertness is below a certain value, and that the proposed iterative algorithm outperforms the benchmark scheme in covertness under the same SOP constraint. © 2017 IEEE.

关键词： Signal to noise ratio

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Efficient Sensitive Data Identification Using Multi-Protocol Automatic Parsing and Vector Classification Based on Feature Fusion

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Informatica (Slovenia) 2024年第22期48卷 55-62页

作者： Hu, Jian Zhang, Lina Lu, Guangqian Xu, Wenqian Jiang, Yixin Yunnan Power Grid Information Center China Southern Power Grid Kunming650217 China Electric Power Research Institute China Southern Power Grid Guangzhou510663 China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou510663 China

At this stage, data has become essential and important information in people's daily life, and how to automate the identification and classification of sensitive attributes of structured datasets in the production environment, etc., has also become a key issue in data privacy protection. This paper proposes an efficient recognition technique for sensitive data based on feature fusion. The method can effectively improve the accuracy of sensitive data identification, realize the identification and classification of sensitive attributes, and take into account the correlation and association between attributes. Moreover, based on the artificial intelligence method, a high-precision and fine-grained identification technology for cross-domain encrypted traffic is constructed, which can support more than five abnormal data access behaviors that can be identified by the data security supervision system, and can support the recovery of traffic with business data not less than 500Mbps. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Data accuracy

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