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IEEE International Conference on Systems, Man and Cybernetics

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image-text retrieval refers to querying the text or image of another modality given an image or text, and its key lies in the ability to accurately measure the similarity between text and image. Most of the existing retrieval methods only utilize the intramodal relations of each modality or the intermodal relations of the two modalities to perform the retrieval task. To address the above problems, we integrate the intra-modal and inter-modal relations and propose a Semantic Relation-based Cross Attention network(SRCAN). In our proposed method, we first mine the possible intra-modal associations between regions in an image and between words in a text to obtain features with semantic relationships, and then capture the fine-grained associations between segments through the cross-attention mechanism. Finally, we balance the intra-modal and inter-modal relationships to improve the performance of the model. Our proposed method is experimentally validated on two datasets, Flickr30K and MS-COCO, and the experimental results show that our method achieves superior results.

关键词： Image segmentation Semantics Bidirectional control Feature extraction Cognition Feeds Cybernetics

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Cross-modal retrieval based on Attention Embedded Variational Auto-Encoder

Cross-modal retrieval based on Attention Embedded Variationa...

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International Joint Conference on Neural networks (IJCNN)

作者： Kaili Zhou Yiyun Xing Yushui Geng Jing Zhao Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This study proposes a cross-modal retrieval technique, which employs an Attention Embedded Variational AutoEncoder (AE-VAE) to tackle the problem of reduced retrieval accuracy resulting from data noise and data absence in cross-modal retrieval tasks. First, the variational auto-encoder(VAE) is used as an infrastructure to transform modal data into latent variables by encoding and decoding different modal features, thus preserving the semantic structure of the raw modal data and suppressing noise and missing information in the data. Secondly, to better capture the important semantic features of image and text modalities, an improved multi-attention module is introduced in the encoder. In addition, a cross-modal alignment module is designed to further align the latent variables of different modalities in the latent space to solve the confusion problem and improve the accuracy and robustness of cross-modal retrieval. Finally, experiments are conducted on four public datasets, CUB, SUN, AwA1 and AwA2, and the experimental results show that our proposed model is effective in improving the accuracy and robustness of retrieval.

关键词： Accuracy Uncertainty Noise Semantics Transforms Probabilistic logic Robustness

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Semantic-Enhanced Attention network for Image-Text Matching

Semantic-Enhanced Attention Network for Image-Text Matching

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International Conference on Computer Supported Cooperative Work in Design

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image-text matching is an important task in cross-modal information processing, which consists of evaluating the similarity between images and text. However, the data of the two modalities have different distributions and representations, which cannot be directly compared. Therefore, the two modalities need to be processed separately. Most existing image-text matching methods extract image fragments and text fragments separately, using an attention mechanism to establish relationships between regions in the image and words in the text. Subsequently, they aggregate the similarity of these images and text fragments. Regardless, not all fragment alignment is meaningful, and irrelevant fragment alignment will bring redundancy and reduce retrieval accuracy. In addition to that, there are contextual relationships between image regions and between text words, which should also be taken into account. In this paper, we simultaneously consider these two aspects and propose a Semantic-Enhanced Attention network (SEAN). It first focuses on each modality, mining contextual relationships between fragments within each modality and aggregating contextual information into visual and textual embeddings. Then, we compute the similarity between all image regions and text fragments, focusing all attention on the region-word pairs with the highest similarity. Finally, we summarize these to infer the similarity between the image and text. Our method achieves competitive results on two generalized image text retrieval datasets, Flickr30K and MS-COCO.

关键词： Visualization Attention mechanisms Federated learning Computational modeling Aggregates Semantics Redundancy

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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

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A Prediction Method for Atmospheric Monitoring Data Based on Transformer and GAN

A Prediction Method for Atmospheric Monitoring Data Based on...

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IEEE Information technology, networking, Electronic and Automation Control Conference

作者： Hongge Zhao Jingli Wang Tixiang Zhang Huijuan Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Jinan Eco-environmental Bureau Pingyin Branch Office Jinan China Shandong Unite Environmental Technology Co Jinan China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

With the rapid development of urbanization and industrialization, atmospheric pollution has become a major environmental problem of global concern. The problem of atmospheric pollution not only destroys the ecological en-vironment of the earth, but also has a serious impact on people's health and development. Therefore, accurate prediction of atmospheric monitoring data is of great significance to environmental management as well as public health. To improve the accuracy of prediction, we propose a prediction method for atmospheric monitoring data based on Trans-former and Generative Adversarial network (GAN). Firstly, data pre-processing is performed on the collected atmospheric monitoring data, and then, constructing a prediction model based on Transformer and GAN for atmospheric monitoring data, aiming at extracting the data features of atmospheric monitoring data by Transformer and generating the data by using the generator of GAN. In addition, we improve the position encoding (PE) of the Transformer in order to it can efficiently extract temporal features of atmospheric monitoring data. Finally, the effectiveness of the proposed method is verified by data from three air monitoring stations in a city.

关键词： Accuracy Pollution Atmospheric modeling Urban areas Predictive models Generative adversarial networks Transformers Feature extraction Data models Monitoring

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Emotion Recognition Empowered Human-Computer Interaction with Domain Adaptation network

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IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Chen, Junxin Northeastern University School of Computer Science and Engineering Shenyang110004 China Dalian University of Technology China The School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China Dalian University of Technology School of Software Dalian116621 China

Multi-modal emotion recognition plays a vital role in the human-computer interaction (HCI) for consumer electronics. Nowadays, many studies have developed multi-modal fusion algorithms for this ***, two challenging issues remain unsolved, i.e., inefficient multi-modal feature fusion and unclear distance in feature *** this end, we develop a novel framework, namely LAFDA-Net, for cross-subject emotion recognition using EEG and eye movement *** is based on low-rank fusion and domain adaptation *** specifically, the multi-modal signals are input into the feature extraction branch in parallel to generate ***, these features are fused by the low-rank fusion branch, reducing complexity and avoiding ***, the fused features are flattened and sent to the classification branch to determine the emotion *** training, these features are input into the domain adaptive branch to bridge the gap between the source domain and target *** benchmark datasets, i.e., SEED, SEED-IV, and SEED-V, are employed for performance *** results demonstrate that the proposed LAFDA-Net is robust, effective, and has advantages over peer methods. © 1975-2011 IEEE.

关键词： Emotion Recognition

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Mix-Net: Automatic Segmentation of Covid-19 ct Images Based on Parallel Design

Mix-Net: Automatic Segmentation of Covid-19 ct Images Based ...

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IEEE International Conference on Image Processing

作者： Aimei Dong Ruixin Wang Guohua Lv Guixin Zhao Yi Zhai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Since the discovery of COVID-19 in late 2019, the viral pneumonia crisis has begun to spread rapidly around the world. Lesion segmentation can remove unnecessary background areas and help doctors diagnose the condition. However, the infected areas showed differences at different stages, and the border between the infected areas and the surrounding tissue was blurred. To solve this problem, a novel COVID-19 lung infection segmentation network (Mix-Net) is designed for the automatic identification of infected areas from chest CT slices. Specifically, first, the local and global features of the infected areas are extracted and interacted with using the mixing block. Then, the features extracted from multiple layers of the encoder are fused and connected to the decoder. Experiments show that Mix-Net outperforms most cutting-edge segmentation models and achieves good segmentation results.

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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Shujiang Xu Fansheng Wang Lianhai Wang Miodrag J. Mihaljević Shuhui Zhang Wei Shao Qihao Huang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Mathematical Institute the Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users’ identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible.

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Chinese named entity recognition method for the field of network security based on RoBERTa

Chinese named entity recognition method for the field of net...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Zhu, Xiaoyan Zhang, Yu Zhu, Lei Hei, Xinhong Wang, Yichuan Hu, Feixiong Yao, Yanni Xi'an Jiaotong University School of Computer Science and Engineering Xi'an710049 China School of Computer Science and Technology Xi'an University of technology Shaanxi Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi'an710048 China Shenzhen Tencent Computer System Co. Ltd Shenzhen518054 China

ISBN: (纸本)9781665441582

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively. © 2021 IEEE.

关键词： network security

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Image super resolution via multi-regularization combining hybrid Tikhonov-TV prior and deep denoiser prior

Image super resolution via multi-regularization combining hy...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiahao Zhang Shengrong Zhao Hu Liang Changchun Wen Chen Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In a real scenario, the image is often corrupted by complex degradation, and a lot of useful information is lost, which makes super-resolution (SR) reconstruction seriously ill-posed. To effectively solve such a problem, it is crucial to correctly exploit image prior knowledge. Although existing deep learning-based methods can obtain excellent results, they cannot deal with the complex degradation effectively, which would lead to the loss of texture details and the destruction of edge details. In this paper, an efficient multi-regularization method for SR is proposed, which can simultaneously exploit both internal and external image priors within a unified framework. The hybrid Tikhonov-TV prior and deep denoiser prior are introduced to constrain the reconstruction process. That is, the proposed model combines the superiority of the piecewise-smooth prior and deep prior. Moreover, an adaptive weight parameter is employed to make the hybrid component more detail-preserving. Experimental demonstrate that the proposed method achieves better performance in image detail protection than advanced methods.

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