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A Blockchain and CP-ABE Based Access Control Scheme with Fine-Grained Revocation of Attributes in Cloud Health

computers, Materials & Continua 2024年第2期78卷 2787-2811页

作者： Ye Lu Tao Feng Chunyan Liu Wenbo Zhang School of Computer&Communication Lanzhou University of TechnologyLanzhou730000China School of Economics&Management Lanzhou University of TechnologyLanzhou730000China School of Computer Science Baoji University of Arts and ScienceBaoji721000China

The Access control scheme is an effective method to protect user data *** access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of failure and lack of trust in the centralized ***,it also brings new problems to the health information in the cloud storage environment,such as attribute leakage,low consensus efficiency,complex permission updates,and so *** paper proposes an access control scheme with fine-grained attribute revocation,keyword search,and traceability of the attribute private key distribution *** technology tracks the authorization of attribute private *** credit scoring method improves the Raft protocol in consensus ***,the interplanetary file system(IPFS)addresses the capacity deficit of *** the premise of hiding policy,the research proposes a fine-grained access control method based on users,user attributes,and file *** optimizes the data-sharing *** the same time,Proxy Re-Encryption(PRE)technology is used to update the access *** proposed scheme proved to be *** analysis and experimental results show that the proposed scheme has higher efficiency and more *** can meet the needs of medical institutions.

关键词： Blockchain access-control CP-ABE cloud health

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MARIC: an efficient multi-agent real-time intention-based communication model for team cooperation

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Neural Computing and Applications 2025年第5期37卷 3925-3942页

作者： He, Xin Ge, Hongwei Hao, Zhangang Hou, Yaqing School of Computer Science and Technology Dalian University of Technology Liaoning Dalian160023 China School of Management Shandong Technology and Business University ShanDong YanTai264003 China

Effective communication is crucial for promoting team cooperation in multi-agent reinforcement learning tasks. Human intention plays a key role in facilitating effective communication, driving individuals to communicate quickly and accurately with team members. In this paper, we propose an efficient multi-agent real-time intention-based communication model for team cooperation called MARIC. In MARIC, an intention correlation network is proposed to drive the agent’s belief about who to communicate with, which considers several influencing factors, e.g. the agent’s situation awareness about each team member and the agent’s purpose inference of other teammates. This network maps the agent’s local observation to real-time communication intention, and quantifies the correlation between the agents regarding each influencing factor to label the necessity of communication. To improve the stability of the training process and avoid redundant information, a message simplicity loss is proposed as a regularization technique. MARIC is evaluated against several existing baselines in two typical multi-agent cooperative tasks: predator–prey and Traffic Junction, and a mixed cooperative-competitive task: Google Research Football. The experimental results show that MARIC outperforms the baselines. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Reinforcement learning

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BEV-Locator:an end-to-end visual semantic localization network using multi-view images

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science China(Information sciences) 2025年第2期68卷 134-150页

作者： Zhihuang ZHANG Meng XU Wenqiang ZHOU Tao PENG Liang LI Stefan POSLAD School of Vehicle and Mobility Tsinghua University School of Information Technology & Management University of International Business and Economics Qcraft Inc. School of Electronic Engineering and Computer Science Queen Mary University of London

Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV(bird-eye-view) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map query sequences. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. This end-to-end model speaks to its broad applicability across different driving environments, including high-speed scenarios. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-Locator is capable of estimating the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052 m, 0.135 m and 0.251° in lateral, longitudinal translation and heading angle degree.

关键词： visual localization semantic map bird-eye-view transformer pose estimation

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Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy

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Chinese Journal of Electronics 2022年第4期31卷 741-751页

作者： KANG Haiyan JI Yuanrui ZHANG Shuxuan School of Information Management Beijing Information Science and Technology University Computer School Beijing Information Science and Technology University

With the popularization and development of social software, more and more people join the social network, which produces a lot of valuable information, but also contains plenty of sensitive privacy information. To achieve the personalized privacy protection of massive social network relational data, a privacy enhancement method for social networks relational data based on personalized differential privacy is proposed. And a dimensionality reduction segmentation sampling(DRS-S)algorithm is proposed to implement this method. First, in order to solve the problem of inefficiency caused by the excessive amount of data in social networks, dimension reduction and segmentation are carried out to divide the data into groups. According to the privacy protection requirements of different users, we adopt sampling method to protect users with different privacy requirements at different levels, so as to realize personalized different privacy. After that, the noise is added to the protected data to satisfy the privacy budget. Then publish the social network data. Finally, the proposed algorithm is compared with the traditional personalized differential privacy(PDP) algorithm and privacy preserving approach based on clustering and noise(PBCN) in real data set, the experimental results demonstrate that the quality of privacy protection and data availability of DRS-S are better than that of PDP algorithm and PBCN algorithm.

关键词： Social network Privacy preserving Dimensionality reduction segmentation Personalized differential privacy DRS-S algorithm

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A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

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Tsinghua science and technology 2024年第1期29卷 99-111页

作者： Yuxuan Cao Difei Zhang Shaoqi Ding Weiyi Zhong Chao Yan School of Computer Science Qufu Normal UniversityRizhao 276826China School of Mathematical Sciences Qufu Normal UniversityQufu 273165China School of Computer Science Qufu Normal UniversityRizhao 270826China College of Economic and Management Shandong University of Science and TechnologyQingdao 250307China

Air pollution is a severe environmental problem in urban *** air quality prediction can help governments and individuals make proper decisions to cope with potential air *** a classic time series forecasting model,the AutoRegressive Integrated Moving Average(ARIMA)has been widely adopted in air quality ***,because of the volatility of air quality and the lack of additional context information,i.e.,the spatial relationships among monitor stations,traditional ARIMA models suffer from unstable prediction *** some deep networks can achieve higher accuracy,a mass of training data,heavy computing,and time cost are *** this paper,we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring *** proposed model consists of three components:(1)an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations;(2)the Empirical Mode Decomposition(EMD)to decompose the air quality time series data into multiple smooth sub-series;and(3)the truncated Singular Value Decomposition(SvD)to compress and denoise the expanded *** results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.

关键词： air quality prediction Empirical Mode Decomposition(EMD) Singular Value Decomposition(SVD) AutoRegressive Integrated Moving Average(ARIMA)

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Credit Risk Prediction Based on Improved ADASYN Sampling and Optimized LightGBM

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Journal of Social Computing 2024年第3期5卷 232-241页

作者： Mei Song He Ma Yi Zhu Mengdi Zhang the School of Computer Science and Technology Jiangsu Normal UniversityXuzhou 221116China the Management Science and Technology Center Jiangsu Normal UniversityXuzhou 221116China

A credit risk prediction model named KM-ADASYN-TL-FLLightGBM(KADT-FLightGBM)is proposed in this ***,to overcome the limitation of traditional sampling methods in dealing with imbalanced datasets,an improved ADASYN sampling with K-means clustering algorithm is ***,the Tomek Links method is used to filter the generated ***,an utilized an optimized LightGBM algorithm with the Focal Loss is employed to training the model using the datasets obtained by the improved ADASYN ***,the comparative analysis between the ensemble model and other different sampling methodologies is conducted on the Lending Club *** results demonstrate that the proposed model effectively minimizes the misclassification of minority classes in credit risk prediction and can be used as a reference for similar studies.

关键词： imbalance data credit risk prediction Focal Loss adaptive hybrid sampling

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Multi-head attention graph convolutional network model:End-to-end entity and relation joint extraction based on multi-head attention graph convolutional network

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CAAI Transactions on Intelligence technology 2023年第2期8卷 468-477页

作者： Zhihua Tao Chunping Ouyang Yongbin Liu Tonglee Chung Yixin Cao School of Computer Science University of South ChinaHengyangChina Department of Computer Science and technology Tsinghua UniversityBeijingChina School of Computer and Information Systems Singapore Management UniversitySingaporeSingapore

At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure *** adjacency matrix constructed by the dependency tree can convey syntactic *** trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic *** the same time,a large amount of irrelevant information will cause *** paper presents a novel end-to-end entity and relation joint extraction based on the multihead attention graph convolutional network model(MAGCN),which does not rely on external *** generates an adjacency matrix through a multi-head attention mechanism to form an attention graph convolutional network model,uses head selection to identify multiple relations,and effectively improve the prediction result of overlapping *** authors extensively experiment and prove the method's effectiveness on three public datasets:NYT,WebNLG,and *** results show that the authors’method outperforms the state-of-the-art research results for the task of entities and relation extraction.

关键词： information retrieval natural language processing

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De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

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Journal of Electronic science and technology 2024年第3期22卷 57-68页

作者： Yan Li Tai-Kang Tian Meng-Yu Zhuang Yu-Ting Sun School of Economics and Management University of Electronic Science and Technology of ChinaChengdu611731China School of Economics and Management Beijing University of Posts and TelecommunicationBeijing100876China School of Electrical Engineering and Computer Science The University of QueenslandBrisbane4072Australia

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various ***,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still *** address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification *** the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing *** on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student *** approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student *** conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

关键词： De-biasing Deep learning Knowledge distillation Model compression

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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An Efficient Bi-modal Fusion Framework for Music Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 999-1015页

作者： Xiao, Yao Ruan, Haoxin Zhao, Xujian Jin, Peiquan Tian, Li Wei, Zihan Cai, Xuebo Wang, Yixin Liu, Liang Southwest University of Science and Technology School of Computer Science and Technology Sichuan Mianyang621010 China Southwest University of Science and Technology School of Mathematics and Physics Sichuan Mianyang621010 China University of Science and Technology of China School of Computer Science and Technology Hefei230000 China Sichuan University of Culture and Arts School of Music and Dance Sichuan Mianyang621000 China Southwest University of Science and Technology School of Economics and Management Sichuan Mianyang621010 China

Current methods for Music Emotion Recognition (MER) face challenges in effectively extracting features sensitive to emotions, especially those rich in temporal detail. Moreover, the narrow scope of music-related modalities impedes data integration from multiple sources, while including multiple modalities often leads to redundant information, which can degrade performance. To address these issues, we propose a lightweight framework for music emotion recognition that improves the extraction of features that are both sensitive to emotions and rich in temporal information and that integrates data from both audio and MIDI modalities while minimizing redundancy. Our approach involves developing two innovative unimodal encoders to learn embeddings from audio and MIDI-like features. Additionally, we introduce a Bi-modal Fusion Attention Model (BFAM) that integrates features from low-level to high-level semantic information across different modalities. Experimental evaluations on the EMOPIA and VGMIDI datasets show that our unimodal networks achieve accuracies that are 6.1% and 4.4% higher than baseline algorithms for MIDI and audio on the EMOPIA dataset, respectively. Furthermore, our BFAM achieves a 15.2% improvement in accuracy over the baseline, reaching 82.2%, which underscores its effectiveness for bi-modal MER applications © 2010-2012 IEEE.

关键词： Data integration

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