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

作者： Wang, Guangjin Wang, Bao Xu, Fuyong Zhu, Zhenfang Wang, Peipei Wang, Ru Liu, Peiyu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model's "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model's ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： KL divergence multi-view semantic representation multimodal sarcasm detection semantic representation enhancement

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PFD-Net: Pyramid Fourier Deformable network for medical image segmentation

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computers in Biology and Medicine 2024年 172卷 108302-108302页

作者： Yang, Chaorong Zhang, Zhaohui College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Hebei Normal University Shijiazhuang050024 China

Medical image segmentation is crucial for accurately locating lesion regions and assisting doctors in diagnosis. However, most existing methods fail to effectively utilize both local details and global semantic information in medical image segmentation, resulting in the inability to effectively capture fine-grained content such as small targets and irregular boundaries. To address this issue, we propose a novel Pyramid Fourier Deformable network (PFD-Net) for medical image segmentation, which leverages the strengths of CNN and Transformer. The PFD-Net first utilizes PVTv2-based Transformer as the primary encoder to capture global information and further enhances both local and global feature representations with the Fast Fourier Convolution Residual (FFCR) module. Moreover, PFD-Net further proposes the Dilated Deformable Refinement (DDR) module to enhance the model's capacity to comprehend global semantic structures of shape-diverse targets and their irregular boundaries. Lastly, Cross-Level Fusion Block with deformable convolution (CLFB) is proposed to combine the decoded feature maps from the final Residual Decoder Block (DDR) with local features from the CNN auxiliary encoder branch, improving the network's ability to perceive targets resembling the surrounding structures. Extensive experiments were conducted on nine publicly medical image datasets for five types of segmentation tasks including polyp, abdominal, cardiac, gland cells and nuclei. The qualitative and quantitative results demonstrate that PFD-Net outperforms existing state-of-the-art methods in various evaluation metrics, and achieves the highest performance of mDice with the value of 0.826 on the most challenging dataset (ETIS), which is 1.8% improvement compared to the previous best-performing HSNet and 3.6% improvement compared to the next-best PVT-CASCADE. Codes are available at https://***/ChaorongYang/PFD-Net. © 2024 Elsevier Ltd

关键词： Convolution

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

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Radon CLF:A Novel Approach for Skew Detection Using Radon Transform

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computer Systems Science & engineering 2023年第10期47卷 675-697页

作者： Yuhang Chen Mahdi Bahaghighat Aghil Esmaeili Kelishomi Jingyi Du Laboratory for Control Engineering Xi’an University of Science&TechnologyXi’anChina Computer Engineering Department Imam Khomeini International UniversityQazvinIran MOE Key Laboratory for Intelligent and Network Security Xi’an Jiaotong UniversityXi’anChina

In the digital world,a wide range of handwritten and printed documents should be converted to digital format using a variety of tools,including mobile phones and ***,this is not an optimal procedure,and the entire document image might be *** conversion effects due to noise,motion blur,and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition(OCR)*** Document Image Analysis Systems(DIAS),skew estimation of images is a crucial *** this paper,a novel,fast,and reliable skew detection algorithm based on the Radon Transform and Curve Length Fitness Function(CLF),so-called Radon CLF,was *** Radon CLF model aims to take advantage of the properties of Radon *** Radon CLF explores the dominating angle more effectively for a 1D signal than it does for a 2D input image due to an innovative fitness function formulation for a projected signal of the Radon *** significant performance indicators,including Mean Square Error(MSE),Mean Absolute Error(MAE),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Measure(SSIM),Accuracy,and run-time,were taken into consideration when assessing the performance of our *** addition,a new dataset named DSI5000 was constructed to assess the accuracy of the CLF *** two-dimensional image signal and the Radon space have been used in our simulations to compare the noise *** results show that the proposed method is more effective than other approaches already in use,with an accuracy of roughly 99.87%and a run-time of 0.048(s).The introduced model is far more accurate and timeefficient than current approaches in detecting image skew.

关键词： Document image analysis skew detection Radon transform pattern recognition

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LAGNet: A Hybrid Deep Learning Model for Automatic Modulation Recognition

LAGNet: A Hybrid Deep Learning Model for Automatic Modulatio...

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IEEE Conference on Wireless Communications and networking

作者： Zhuo Li Guangyue Lu Yuxin Li Hao Zhou Huan Li Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Automatic Modulation Recognition (AMR) is becoming increasingly crucial in the industrial internet, and it ensures more reliable communication for devices within this vast and intricate network. Although recent application of neural networks, notably Graph Convolutional network (GCN) in the AMR domain shows promising results, it fails to account for temporal features within modulation signals, resulting in a loss of recognition precision. Given that modulation signals possess both time and space features, this paper proposes a hybrid deep learning model named LAGNet, which combines Long Short-Term Memory (LSTM) and GCN. The model uses an attention mechanism to map the LSTM outputs into a graph and then employs the GCN to extract the spatial features of the signal. Subsequently, the temporal features and the spatial features are combined together for modulation signal classification. Experimental results show that LAGNet not only outperforms several advanced models in classification accuracy across all signal-to-noise ratios, but also requires fewer learnable parameters.

关键词： Deep learning Accuracy Neural networks Modulation Pattern classification Feature extraction Reliability

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FDSC-YOLOv8:Advancements in Automated Crack Identification for Enhanced Safety in Underground engineering

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computer Modeling in engineering & Sciences 2024年第9期140卷 3035-3049页

作者： Rui Wang Zhihui Liu Hongdi Liu Baozhong Su Chuanyi Ma Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Qilu Transportation Shandong UniversityJinan250100China Geotechnical and Structural Engineering Center Shandong UniversityJinan250061China Shandong Lairong High-Speed Railway Co. Ltd.Weihai264200China Shandong High-Speed Group Co. Ltd.Jinan250014China

In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel ***,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack *** paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural ***,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional ***,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small ***,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak ***,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target *** FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.

关键词： Crack segmentation improved YOLOv8 engineering applications feature extraction

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Smart Contract-Based Auditing of Edge Data for Vehicular networks

Smart Contract-Based Auditing of Edge Data for Vehicular Net...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Yu Zhao Yangguang Tian Chunbo Wang Xiaoqiang Di Hui Qi Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun China School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the development of vehicular networks and cloud-edge collaborative technologies, a large amount of vehicle data is collected at edge nodes (Edge Node, EN) for analysis and decision-making. However, edge data faces challenges in terms of integrity and security. Data owners (Data Owner, DO) should delegate auditors to periodically verify the integrity of the data. However, existing verification methods have not yet addressed issues related to verifiers forging evidence and fair payment. This paper proposes a smart contract-based edge data integrity verification scheme. An audit tree based on lattice hashing is designed, allowing the smart contract to initiate multiple verification challenges while only storing a complete label, thus reducing storage overhead. The homomorphic additivity of lattice hashing supports arbitrary data segmentation as challenges, effectively preventing EN from forging evidence. This scheme also designs two smart contract arbitration algorithms to ensure fair payment. Experimental comparisons show that this scheme effectively resolves the trust issues related to EN and ensures fair payment among EN, CSP, and DO.

关键词： Fabrication Cloud computing Data integrity Smart contracts Collaboration Lattices security Sustainable development Faces Internet of Vehicles

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Membership Inference Attacks via Dynamic Adversarial Perturbations Reduction

Membership Inference Attacks via Dynamic Adversarial Perturb...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Zehua Ding Youliang Tian Guorong Wang Jinbo Xiong Jianfeng Ma State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China School of Cyber Engineering Xidian University Xian China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Existing membership inference attacks (MIAs) based on adversarial attacks typically introduce excessively large adversarial perturbations to change the model predictions. However, such methods are not only likely to obscure the characteristic memory of the model regarding member data but also blur the subtle characteristic differences between member and non-member data. To address this issue, we propose a novel dynamic adversarial perturbation reduction MIA (DAPR-MIA), which aims to enhance the ability to identify the membership of samples by reducing the perturbation strength. Specifically, DAPR-MIA first conducts a fine-grained analysis of the gradient components of conventional adversarial examples, assessing the impact of each element on the model decision to generate a sensitivity mask. This mask identifies dimensions within adversarial examples that significantly influence model predictions, referred to as sensitive dimensions. Then, by dynamically reducing the perturbation size in these sensitive dimensions, DAPR-MIA gradually approaches the minimum perturbation required to change the prediction result of samples, maximizing the retention of membership features within the sample. Finally, the deviation of the prediction results between the original sample and the adversarial example after perturbation attenuation on the target model is measured to infer whether the sample belongs to a member data. Experimental results show that DAPR-MIA significantly outperforms existing methods on multiple datasets and deep models, demonstrating state-of-the-art attack accuracy.

关键词： Privacy Sensitivity Perturbation methods Computational modeling Predictive models Attenuation measurement Attenuation Data models Robustness security

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A Trusted Privacy-Preserving Model for Cross-Chain Transactions Based on zk_SNARKs

A Trusted Privacy-Preserving Model for Cross-Chain Transacti...

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International Conference on networking and network Applications (NaNA)

作者： Yichuan Wang Jianhuan Tie Xinhong Hei Li Zhao Xiaohui Zhang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi'an China

Currently, the privacy protection technology of blockchain is not mature enough, such as user data leakage and lack of anonymity exist, and these problems are especially serious when conducting cross-chain transactions. In this paper, we propose a method of cross-chain transactions based on zk_SNARKs in a trusted environment for cross-chain transactions for privacy protection. Cross-chain transactions are performed through the features of zk_SNARKs such as efficiency, privacy, and verifiability. zk_SNARKs needs to generate trusted settings with the help of a third party during the transaction, and the process has serious privacy issues, and the private parameters generated during the transaction may be maliciously attacked and thus obtained by attackers. To address this problem, we use a hardware technology SGX for trusted computing to encrypt and store some private parameters generated by zk_SNARKs, thus ensuring the security of cross-chain transactions. The experimental results show that the privacy protection scheme using SGX for cross-chain transactions takes less time than the traditional transaction privacy protection, with a verification time of 1.2s for a number of 10 sidechains, and is more efficient than the traditional cross-chain transaction privacy protection.

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Evolutionary Multitasking Based on Team Learning Strategy 6

Evolutionary Multitasking Based on Team Learning Strategy

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Li, Wei Xu, Qingzheng Gao, Xinyu Wang, Lei Xi'an University of Technology School of Computer Science and Engineering Xi'an China National University of Defense Technology College of Information and Communication Wuhan China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665453110

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm. © 2022 IEEE.

关键词： Evolutionary algorithms

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