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Journal of Network Intelligence 2023年第2期8卷 478-490页

作者： Wang, Dong-Yang Lin, Kai-Biao Wu, Zhi-Rong Lu, Jiang-Tao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Provincial University Putian351100 China Xiamen Jingpei Software engineering Co. LTD Xiamen361008 China

Most graphs or networks in the real world are inherently heterogeneous and involve different types of nodes and relationships. Typically, existing models define multiple metapaths in the heterogeneous graph to capture complex semantic relationships and guide neighbor selection. However, such models ignore node characteristics and do not consider information about intermediate nodes. Alternatively, in such models, the target node is assimilated by neighboring nodes or only information about a metapath is considered. Thus, in this paper, we proposed the neighborhood similarity-based metapath aggregation graph neural network (MANS-GNN) model. The proposed MANS-GNN first uses node mapping for the input of node features. Second, to avoid embedding and exces-sive assimilation between different node types, a similarity-aware neighborhood selector based on reinforcement learning is employed to select the most similar neighborhood for the target node. Then, a local aggregation module is employed to merge intermediate semantic nodes. Finally, global aggregation is employed to merge information from in-dividual metapaths. The proposed MANS-GNN model was evaluated experimentally in node classification and node clustering tasks on two heterogeneous graph datasets. Experimental results demonstrated that the proposed MANS-GNN model obtains more accurate predictions than the baseline model. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Reinforcement learning

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Interactive Visualization of Temporal Brain Connectivity data based-on Frequent Feature Mining 35

Interactive Visualization of Temporal Brain Connectivity Dat...

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35th International Conference on Software engineering and Knowledge engineering, SEKE 2023

作者： Zhang, Guangwei Jing, Ming Liu, Yunjing Zhang, Li Dong, Anming Yu, Jiguo School of Computer Science and Technology Qilu University of Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

Medical data visualization is instrumental in assisting disease diagnosis and exploring brain function and structure. In this paper, we constructed a brain connectivity network using changes in BOLD signals at different time intervals and identified frequent characteristics to help doctors quickly pinpoint areas of interest. To study the changes in connectivity between brain regions, we visualize frequent sequences and compare them, highlighting important temporal features of patient brain areas. This makes the study and analysis of fMRI data more convenient and assists doctors in investigating abnormalities in the connections between brain functional areas. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Brain

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ModelLock: Locking Your Model With a Spell 24

ModelLock: Locking Your Model With a Spell

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32nd ACM International Conference on Multimedia, MM 2024

作者： Gao, Yifeng Sun, Yuhua Ma, Xingjun Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Cs Fudan University Shanghai China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400706868

This paper presents a novel model protection paradigm Model Locking that locks the performance of a finetuned model on private data to make it unusable or unextractable without the right key. Specifically, we proposed a diffusion-based framework dubbed ModelLock that explores text-guided image editing to transform the private finetuning data into unique styles or blend new objects into the background. A model finetuned on this edited dataset will be locked and can only be unlocked by the key prompt, i.e., the same text prompt used to edit the data. We conduct extensive experiments on both image classification and segmentation tasks and show that 1) ModelLock can effectively lock finetuned models without significantly reducing their unlocked performance, and more importantly, 2) the locked model cannot be easily unlocked without knowing both the key prompt and the diffusion model. Our work opens up a new direction for intellectual property protection of private models. © 2024 ACM.

关键词： Keys (for locks)

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research on Deep Learning-Based Algorithms for Medical Image Characterisation 2

Research on Deep Learning-Based Algorithms for Medical Image...

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Yu, Yunxia Qi, Zhiyong Xu, Ling Zhou, Xiong Jingchu University Of Technology Big Data Research Center Jingmen China College Of Computer Science Wuhan Vocational College Of Software And Engineering Wuhan China Shayang People's Hospital Jingmen China

ISBN: (纸本)9798350381665

Lung disease is a worldwide health problem, and accurate image diagnosis of lung disease is crucial for patient treatment and management. In recent years, deep learning has made significant progress in the field of medical image analysis, in which Graph Neural Networks (GNNs), as a special deep learning model, show great potential in lung disease diagnosis. In this study, we propose a graph neural network-based model for lung disease diagnosis, aiming to automate the analysis of lung images and lesion detection. First, we represent the lung images as a graph structure, where each pathogenic region represents a node in the graph. Then, the adjacency matrix and node features of the graph are constructed, and the image is subjected to feature extraction and representation learning using a graph neural network, which enables the model to capture the structural and contextual information in the image. To further improve the performance of the model, we introduce multilayer graph convolution and multilayer perceptron to enhance the sensitivity and differentiation ability of the graph neural network for lung lesions. We conducted experimental evaluations based on publicly available lung image datasets and compared them with traditional machine learning methods as well as other deep learning models. The experimental results show that the proposed graph neural network model achieves excellent performance on the lung disease diagnosis task. © 2024 IEEE.

关键词： Multilayers

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding 48

Tracking Objects and Activities with Attention for Temporal ...

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

作者： Xiong, Zeyu Liu, Daizong Zhou, Pan Zhu, Jiahao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9781728163277

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets' behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed. © 2023 IEEE.

关键词： computer vision

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FRMT: RMT with Frequency Feature learning Framework for land cover classification 3

FRMT: RMT with Frequency Feature learning Framework for land...

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3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Ou, Zhihui Chen, Fang Wang, Lei Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

The classification of land cover is of great significance in the management and sustainable development of land resources. In recent years, deep learning methods have made remarkable progress in the field of remote sensing image processing. However, due to the complex difference in structure and scale of surface objects in remote sensing images, most of the current methods are prone to the problem of unclear boundary extraction, and the classification results of different scale surface objects are poor. In this letter, we proposed FRMT by introducing frequency domain information into RMT framework. A frequency feature learning module (FFLM) is proposed. The frequency domain features are decomposed into high frequency and low frequency features, and the frequency features are learned by spiking convolution to strengthen the model's learning of boundary and different scale features. We performed experimental validation on the very challenging multi-class remote sensing dataset GID-15 and Huawei Ascend Cup competition dataset, and the experimental results show that our model achieves the best results compared to five other recently proposed models, which mIoU achieves 77.91% and 71.85%, respectively. © 2025 SPIE.

关键词： Frequency domain analysis

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Traceable Ring Signature Scheme in Internet of Vehicles Based on Lattice Cryptography 21

Traceable Ring Signature Scheme in Internet of Vehicles Base...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liao, Wei Han, Lansheng Chen, Peng Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users' public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners. © 2023 IEEE.

关键词： Cryptography

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research on traffic vehicle target detection method based on improved YOLOv7 24

Research on traffic vehicle target detection method based on...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Zhang, Feng Sun, Quan Yuan, Shuai School of Electrical and Control Engineering Shenyang Jianzhu University China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center China

ISBN: (纸本)9798400716720

With the increase in the number of vehicles in our country, traffic accidents have become frequent. The real-time detection of dense traffic vehicles is particularly important, which can promote the development of autonomous driving and lay a good foundation for intelligent transportation. Aiming at the problems such as high vehicle overlap rate, high small target missing rate and poor real-time detection in traffic vehicle data set, a traffic vehicle target detection method based on improved YOLOv7 was proposed. Firstly, the neck network structure of the original YOLOv7 is lightweight and introduced into the GhostSlimPAN paradigm structure to reduce the number of model parameters and operation cost, and improve the accuracy of detection of small target vehicles. Then, the original loss function CIoU is improved to replusion loss function to reduce the missed and false detection rate caused by mutual occlusion between vehicles. In order to verify the effect of the improved model, the vehicle dataset was selected for testing and verification. The experimental results show that compared with the original YOLOv7 algorithm, the mAP@0.5 of the improved YOLOv7 algorithm on this dataset reaches 88.2%, which is 1.8% higher than that of the benchmark network. The improved algorithm can be applied to vehicle target detection in daily environment, providing key technical support for intelligent transportation and autonomous driving. © 2024 ACM.

关键词： Autonomous vehicles

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research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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Hybrid feature-based neural network regression method for load profiles forecasting

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Energy Informatics 2025年第1期8卷 1-20页

作者： Satan, Aidos Zhakiyev, Nurkhat Nugumanova, Aliya Friedrich, Daniel Department of Science and Innovation Astana IT University Astana010000 Kazakhstan Big Data and Blockchain technologies Research Center Astana IT University Astana Kazakhstan School of Engineering Institute for Energy Systems University of Edinburgh EdinburghEH93DW United Kingdom Department of Energy Engineering University of Genova Savona17100 Italy

This study addresses the critical need for improved demand forecasting models that can accurately predict energy consumption, particularly in the context of varying geographical and climatic conditions. The work introduces a novel demand forecasting model that integrates clustering techniques and feature engineering into neural network regression, with a specific focus on incorporating correlations with air temperature. Evaluation of the model’s efficacy utilized a benchmark dataset from Tetouan, Morocco, where existing forecasting methods yielded RMSE values ranging from 6429 to 10,220 [MWh]. In contrast, the proposed approach achieved a significantly lower RMSE of 5168, indicating its superiority. Subsequent application of the model to forecast demand in Astana, Kazakhstan, as a case study, showcased its efficacy further. Comparative analysis against a baseline neural network method revealed a notable improvement, with the proposed model exhibiting a MAPE of 5.19% compared to the baseline’s 17.36%. These findings highlight the potential of the proposed approach to enhance demand forecasting accuracy, particularly across diverse geographical contexts, by leveraging climate-related inputs, the methodology also demonstrates potential for broader applications, such as flood forecasting, agricultural yield prediction, or water resource management. © The Author(s) 2025.

关键词： Demand response

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