检索结果-内蒙古大学图书馆

Cloud-Based Privacy-Preserving Medical Images Storage Scheme with Low Consumption

IEEE Transactions on Multimedia 2025年 27卷 3556-3570页

作者： Yang, Yaolin He, Hongjie Feng, Zhuo Chen, Fan Yuan, Yuan Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China

For the security risks and high transmission/storage consumption in cloud-based medical images storage systems (CMISS), reversible data hiding in encrypted images (RDHEI) provide an effective solution. Nevertheless, challenges persist concerning the security risks cause by key transmission and the large file size of encrypted medical images. Consequently, a cloud-based privacy-preserving medical images storage scheme with low consumption is proposed in this paper. First, RDHEI is applied to CMISS, where image encryption achieves privacy protection, reversible data hiding eliminates extra space consumption by index data self-hiding, and the reversibility enables lossless recovery and extraction of medical images and index data. Then, hybrid encryption is designed to achieve high security. The security of encrypted images is guaranteed by combining a one-time cryptosystem with symmetric XOR encryption, which makes our scheme can resist various attacks. Time-varying key used in XOR is encrypted by asymmetric RSA, and only public key is used in RSA, avoiding the risk of private key transmission. Finally, to reduce the file size of encrypted images and achieve low consumption, context Huffman coding is proposed to adaptively selects the block coding method by context and thresholds, and has at most 98,056 bits shorter than Huffman coding in encoded stream length. Experimental results show that the proposed scheme has better performance in terms on security, consumption, and reversibility. The minimum compression ratio in databases is 32.46%, which is 2.63% lower than the existing schemes. And the medical image and index data can be restored lossless. © 1999-2012 IEEE.

关键词： Encryption Cryptography Image coding Medical diagnostic imaging Indexes Servers Cloud computing Medical services Huffman coding Protection

来源：评论

学校读者我要写书评

暂无评论

Chaotic Map-Based Authentication and Key Agreement Protocol with Low-Latency for Metasystem

引用

Computers, Materials & Continua 2024年第3期78卷 4471-4488页

作者： Guojun Wang Qi Liu School of Electronics&Information Engineering Nanjing University of Information Science&TechnologyNanjing210044China Yancheng Polytechnic College Yancheng224000China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of SoftwareNanjing University of Information Science and TechnologyNanjingChina

With the rapid advancement in exploring perceptual interactions and digital twins,metaverse technology has emerged to transcend the constraints of space-time and reality,facilitating remote AI-based *** this dynamic metasystem environment,frequent information exchanges necessitate robust security measures,with Authentication and Key Agreement(AKA)serving as the primary line of defense to ensure communication ***,traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the *** address this challenge and enable nearly latency-free interactions,a novel low-latency AKA protocol based on chaotic maps is *** protocol not only ensures mutual authentication of entities within the metasystem but also generates secure session *** security of these session keys is rigorously validated through formal proofs,formal verification,and informal *** confronted with the Dolev-Yao(DY)threat model,the session keys are formally demonstrated to be secure under the Real-or-Random(ROR)*** proposed protocol is further validated through simulations conducted using VMware workstation compiled in HLPSL language and C *** simulation results affirm the protocol’s effectiveness in resisting well-known attacks while achieving the desired low latency for optimal metaverse interactions.

关键词： Metasystem authentication and key agreement chaotic map secure communication

来源：评论

学校读者我要写书评

暂无评论

Uplink Performance of Cell-Free Symbiotic Radio With Hardware Impairments for IoT

引用

IEEE Internet of Things Journal 2025年第13期12卷 23418-23433页

作者： Sun, Qiang Zhou, Yu Shen, Yushi Li, Feiyang Chen, Xiaomin Li, Dong Zhang, Jiayi Nantong University School of Information Science and Technology Nantong226019 China NovNet Computing System Tech Company Beijing100081 China Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China

Cell-free massive multiple-input multiple-output symbiotic radio (CF-SR) has recently been introduced as a promising solution for the Internet of Things (IoT), offering cost advantages and more uniform coverage performance for user devices. However, most previous studies assume perfect hardware, which is impractical in IoT systems. In this paper, we investigate the uplink performance of CF-SR systems in the presence of hardware impairments (HWIs). We adopt two novel schemes for hybrid combining, namely hybrid maximum ratio (HMR) and hybrid local minimum mean square error (HL-MMSE), both of which effectively enhance the spectral efficiency (SE) of the backscattering link. We derive closed-form expressions for the achievable SE of both conventional MR and HMR combining schemes, taking into account the imperfect channel state information (CSI) and HWIs. The simulation results that both the direct and backscattering links are primarily limited by HWIs including multiplicative and additive distortions from the device side. We apply the differential evolution (DE) algorithm for power control to maximize the minimum SE, and the results show that the DE algorithm improves the minimum SE by approximately 52%, avoiding further degradation of the SE of the weakest device due to the impact of HWIs. © 2014 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Inductive Lottery Ticket Learning for Graph Neural Networks

引用

Journal of Computer science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

来源：评论

学校读者我要写书评

暂无评论

Road Traffic Accident Prediction using Deep Learning 1

Road Traffic Accident Prediction using Deep Learning

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Girija, M. Divya, V. School of Computing Science Vistas Department of Computer Science Chennai India School of Computing Science Vistas Department of Information Technology Chennai India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Maintaining public safety and reducing the effects of unplanned incidents need real-time accident prevention. This research work presents a comprehensive plan for developing an advanced computer vision-based accident-avoidance system. The proposed system's primary data source is live video recordings, and it employs a number of procedures to detect anomalies in the environment and issue alerts in a timely manner. The proposed approach begins with the extraction of frames from the continuous video stream in order to enhance the relevance and quality of the returned frames. Effective image preparation techniques follow next. Data augmentation is then used to diversify the dataset and increase the model's generalization ability. In order to enable prompt reaction and intervention, the proposed system instantly notifies users when it detects irregularities, such as potential accidents or dangerous situations. The proposed method is then implemented using Python software. Utilizing a rule-based system, it delivers dynamic recommendations to drivers with enhanced road safety through adaptive speed limits, route suggestions, and real-time hazard alerts through car dashboard. The proposed modified YOLOv8 method shows better accuracy with 98.4%, which is 11.85% higher when compared with Faster RCNN, FAYOLO and FESSD. YOLO-v8 results in a powerful toolkit for accurate object tracking and recognition, guaranteeing a proactive and responsive system. The proposed model's evaluations show the way the method may be utilized to raise public safety by anticipating anomalies and preventing accidents. © 2024 IEEE.

关键词： Video recording

来源：评论

学校读者我要写书评

暂无评论

Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

引用

Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. Data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A DECOUPLED,LINEARLY IMPLICIT AND UNCONDITIONALLY ENERGY STABLE SCHEME FOR THE COUPLED CAHN-HILLIARD SYSTEMS

引用

Journal of Computational Mathematics 2025年第3期43卷 708-730页

作者： Dan Zhao Dongfang Li Yanbin Tang Jinming Wen School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China College of Information Science and Technology Jinan UniversityGuangzhou 510632China

We present a decoupled,linearly implicit numerical scheme with energy stability and mass conservation for solving the coupled Cahn-Hilliard *** time-discretization is done by leap-frog method with the scalar auxiliary variable(SAV)*** only needs to solve three linear equations at each time step,where each unknown variable can be solved *** is shown that the semi-discrete scheme has second-order accuracy in the temporal *** convergence results are proved by a rigorous analysis of the boundedness of the numerical solution and the error estimates at different *** examples are presented to further confirm the validity of the methods.

关键词： Coupled Cahn-Hilliard system Leap-frog method Scalar auxiliary variable Error estimate

来源：评论

学校读者我要写书评

暂无评论

Cognition2Vocation: meta-learning via ConvNets and continuous transformers

引用

Neural computing and Applications 2024年第21期36卷 12935-12950页

作者： Kamran, Sara Hosseini, Saeid Esmailzadeh, Sayna Kangavari, Mohammad Reza Hua, Wen School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Computing and Information Technology Sohar University Sohar Oman Department of Computing The Hong Kong Polytechnic University Hong Kong

Estimating the suitability of individuals for a vocation via leveraging the knowledge within cognitive factors comes with numerous applications: employment resourcing, occupation counseling, and workload management. Accordingly, the enterprises aim to hire the most suitable person from a massive array of similar applicants, maximizing performance and minimizing the gap between strategic indicators and predefined targets. While cognitive factors signify the best-suited person from similarly skilled workers, inferring pertinent latent cues from noisy and growing social network contents is time-intensive. To tackle the challenges involved, we propose a framework that, on the one hand, extends influential features based on the correlations between cognitive cues and, on the other hand, leverages a novel continuous transformer to mitigate the overlapping and approximation issues in discrete modeling. Rather than relying on discrete patterns that may evolve frequently, we use continuous elements that include not only numerous aggregating components but also sense minor irregular fluctuations. In a hybrid manner, we fuse multiple base models to transfer a higher representation to the meta-learning unit, agglomerating outputs from gradient boosters and the ConvNets. The experimental results show that our proposed framework can outperform trending vocation estimation methods by 1.36% in F1-Score and approximately 1% in accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Knowledge management

来源：评论

学校读者我要写书评

暂无评论

Real-time Asset False Matching Detection via Attentive Siamese Networks 6

Real-time Asset False Matching Detection via Attentive Siame...

引用

6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Miao, Ziwan Zhang, Li School of Computing Beijing Information Science & Technology University Beijing China School of Management Science and Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9798331541750

Accurate and efficient asset matching is crucial for remote management and diversified investment entities. To address the challenges of low efficiency, high labor costs, and false matching, this paper proposes a real-time asset false matching detection method based on an attentive Siamese network. By integrating the Convolutional Block Attention Module (CBAM) into a Siamese neural network framework, the model enhances feature extraction capabilities, focusing on discriminative regions within asset images. Pretrained weights are transferred using transfer learning, and data augmentation techniques are applied to enhance the model's generalization ability. Experiments on various types of asset data demonstrate that the proposed method achieves an accuracy of 92.42% and an F1 score of 92.48%, significantly improving the precision and efficiency of asset similarity calculations. This research not only advances asset identification and matching but also showcases exceptional performance in intelligent asset inventory, providing reliable support for asset management. Future work will focus on improving model robustness and optimizing the network structure for large-scale and complex environments. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) for Depression Recognition With Facial Expression and Pupil Diameter

引用

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 3989-4000页

作者： Yang, Minqiang Wu, Yushan Tao, Yongfeng Hu, Xiping Hu, Bin Lanzhou University School of Information Science and Engineering Lanzhou730000 China Beijing Institute of Technology Beijing100081 China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Gansu 730000 China Beijing Institute of Technology School of Medical Technology Beijing100811 China

Facial expressions have been widely used for depression recognition because it is intuitive and convenient to access. Pupil diameter contains rich emotional information that is already reflected in facial video streams. However, the spatiotemporal correlation between pupillary changes and facial behavior changes induced by emotional stimuli has not been explored in existing studies. This paper presents a novel multimodal fusion algorithm - Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) to optimize the feature space and build a more robust depression recognition model, which innovatively combines the spatiotemporal relevance and complementarity between facial expression and pupil diameter features. TSTCCA explores the interaction between trials and obtains an effective fusion representation of two modalities from a trial subset related to depression. The experimental results show that TSTCCA achieves the highest accuracy of 78.81% with the subset of 25 trials. © 2013 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：