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Hybrid chaotic encryption algorithm for securing DICOM systems

International Journal of Performability Engineering 2019年第5期15卷 1436-1444页

作者： Jiao, Ge Zhou, Sheng Li, Lang Zou, Yi College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Environment Protection and Safety Engineering University of South China Hengyang421002 China

The DICOM system contains patients' personal privacy and may present a risk of information leakage in telemedicine diagnostics. In order to solve the security problem of the DICOM system, we propose a medical image encryption method based on the cross-diffusion of the logistic map and the Chebyshev map. We first parse the DICOM image and read the data elements, and then we use the logistic map and the Chebyshev map to generate a key. Then, we cross-use these two sequences for further image encryption diffusion. Finally, we conduct in-depth experiments and comparisons with existing algorithms to validate the characteristics of high security and low correlation of adjacent pixels of the proposed encryption algorithm. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Medical imaging

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Link perspective based network embedding for link prediction 6

Link perspective based network embedding for link prediction

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6th International Conference on Robotics and Artificial Intelligence, ICRAI 2020

作者： Ni, Qixuan Ma, Lina Ye, Yaling Wang, Yuyao Bu, Zhan School of Information Engineering Nanjing University of Finance and Economics Nanjing China WinGin Business-Intelligence Academy Nanjing Co. Ltd Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China

ISBN: (纸本)9781450388597

Link prediction is an important task attracting much attention in the field of complex network, and it can be applied to many real-world scenarios such as recommendation engines, protein-protein interactions prediction, and stock index prediction. Recently, network embedding, which learns low-dimension latent representations of vertexes, has attracted considerable research efforts. It provides a new feasible solution to boost the accuracy of link prediction by preserving the rich structure information of the network. Most existing methods for network embedding depend exclusively on the perspective of nodes, but seldom focus on the perspective of links. In practice, different perspectives of network can bring us different information. As our empirical analysis shows, random walking from node perspective and link perspective of network could bring us different node sampling results, which may mean different partitions of communities. Thus, we designed a new network embedding approach for link prediction which incorporates the perspectives of both nodes and links. The aim of our method is to supplement the structure information from the perspective of links by using line graph, which improve the validity of nodes representations for link prediction. We compare our method with other popular approaches on four real-world networks and the experiment results demonstrate that our method improves performance in three link tasks. © 2020 Association for Computing Machinery. All rights reserved.

关键词： Forecasting

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Correlation-aware deep generative model for unsupervised anomaly detection

arXiv

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arXiv 2020年

作者： Fan, Haoyi Zhang, Fengbin Wang, Ruidong Xi, Liang Li, Zuoyong School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications. However, most existing methods neglect the complex correlation among data samples, which is important for capturing normal patterns from which the abnormal ones deviate. In this paper, we propose a method of Correlation aware unsupervised Anomaly detection via Deep Gaussian Mixture Model (CADGMM), which captures the complex correlation among data points for high-quality low-dimensional representation learning. More specifically, the relations among data samples are correlated firstly in forms of a graph structure, in which, the node denotes the sample and the edge denotes the correlation between two samples from the feature space. Then, a dual-encoder that consists of a graph encoder and a feature encoder, is employed to encode both the feature and correlation information of samples into the low-dimensional latent space jointly, followed by a decoder for data reconstruction. Finally, a separate estimation network as a Gaussian Mixture Model is utilized to estimate the density of the learned latent vector, and the anomalies can be detected by measuring the energy of the samples. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed *** Codes 68T30 Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Automated segmentation of intraretinal cystoid macular edema based on Gaussian mixture model

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Journal of Innovative Optical Health Sciences 2020年第1期3卷 35-47页

作者： Jinghong Wu Sijie Niu Qiang Chen Wen Fan Songtao Yuan Dengwang Li Shandong Key Laboratory of Medical Physics and Image Processing&Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and Electronics Shandong Normal University Jinan 250358P.R.China School of Information Science and Engineering University of Jinan Jinan 250022P.R.China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P.R.China Department of Ophthalmology the First Affiliated Hospital with Nanjing Medical University Nanjing 210094P.R.China

We introduce a method based on Gaussian mixture model(GMM)clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy(DR)from spectral domain optical coherence tomography(SD-OCT)images in this ***,each B-scan is segmented using GMM *** original chustering results are refined using location and thickness ***,the spatial information among every consecutive five B-scans is used to search potential ***,the improved level-set method is used to obtain the accurate *** high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

关键词： Gaussian mixture model level-set spectral domain optical coherence tomography(SD-OCT) segmentation

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An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

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computers, Materials & Continua 2019年第3期58卷 711-725页

作者： Jin Wang Yu Gao Wei Liu Wenbing Wu Se-Jung Lim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114China School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China College of Information Engineering Yangzhou UniversityYangzhou225000China Department of Computer Engineering Chonnam National UniversityYeosu596597Korea.

Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many *** provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of *** clustering process takes sensors’location and residual energy into consideration to elect suitable cluster ***,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to *** experimental results display that the presented method can avoid long distance communicate between sensor ***,this algorithm reduces energy consumption effectively and improves package delivery rate.

关键词： Internet of things wireless sensor networks clustering mobile data collection timer.

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Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning

arXiv

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arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis

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Application of R-FCN algorithm in machine visual solutions on tensorflow based 5th

Application of R-FCN algorithm in machine visual solutions o...

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5th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2018

作者： Zhang, Yumeng Ma, Yanchao Zhang, Fuquan Beijing Information Science Technology University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9783030037659

This paper presents a solution based on Tensorflow platform and R - FCN deep learning model about self-driving cars image processing. Through the Supervised learning of data sets, make them exercise the image segmentation and recognition of information, thus to self-driving cars driving decision-making support. © Springer Nature Switzerland AG 2019.

关键词： computer vision

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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Social Emergency Event Judgement based on BiLSTM-CRF 19

Social Emergency Event Judgement based on BiLSTM-CRF

引用

19th Chinese National Conference on Computational Linguistic, CCL 2020

作者： Hu, Huijun Wang, Cong Dai, Jianhua Liu, Maofu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In recent years, classification and rating of social emergency event have attracted more and more attentions in emergency management. However, most of the current studies adopt the rule-based methods to identify the evidences for event judgement, and have troubles in event judgement due to the complexity of social emergency event composition and the flexibility of language description. Inspired by the idea of event extraction, this paper has proposed the event judgement method via BiLSTM (Bidirectional Long-Short Term Memory) and CRF (Conditional Radom Fields) based on event classification and evidence extraction. The social emergency event classification is carried out firstly, and then the event evidences are extracted based on event type. In the end, the rating of social emergency event is judged with the attention mechanism and the combination of event type and evidence. Experimental results show that the proposed method is more robust than rule-based ones, and effective in the social emergency event judgement. © 2020 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Extraction

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BCDAS: Blockchain-assisted Classifiable Data Auditing Scheme with Dynamic Operations

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Blockchain: Research and Applications 2025年

作者： Yuxin Cui Chen Wang Bing Yang Wei Tong Jian Shen School of Information Science and Engineering (School of Cyber Science and Technology) Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial Key Laboratory of Digital Fashion and Data Governance Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing 312000 Zhejiang China School of Computer Science Nanjing University of Information Science Technology Nanjing 210044 Jiangsu China

As cloud computing gains widespread adoption, cloud storage services have become the primary means of data management for users. Authenticated data structures (ADS) are a novel computational model designed to address data authentication problems in distributed environments. With the growing demand for robust data security, vulnerability detection in storage systems has become a critical area of focus to ensure resilience against potential threats. However, traditional ADS, while ensuring consistency between cloud data and source data, have limitations in handling dynamic data operations on multiple types of files, storage space expansion, and single-point failure issues. To tackle these issues, this paper proposes a blockchain-assisted classifiable data auditing scheme with dynamic operations. First, trapdoor hash functions are used to construct a binary tree. During dynamic data operations, the impact of hash updates is confined to a subset of nodes, ensuring global stability and reducing computational resource consumption. Second, innovative data structures and verification mechanisms are introduced, reducing the risk of single-point failures by decentralizing the dependency on verification paths. Finally, data types are confirmed based on data identifiers, and corresponding path information is recorded, enabling efficient and rapid dynamic operations on specific types of files within multi-source data. Both security analysis and performance assessment demonstrate that BCDAS conducts data auditing for with reliability and efficiency.

关键词： Data auditing Dynamic operations Blockchain Trapdoor hash function

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