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Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

computers, Materials & Continua 2023年第7期76卷 849-859页

作者： Baowei Wang Neng Wang Yuxiao Zhang Zenghui Xu Junhao Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044JiangsuChina Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044JiangsuChina

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data *** speaking,EHRs are widely used in blockchain-based medical data *** are valuable private assets of patients,and the ownership belongs to *** recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit *** order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate *** paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted *** the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical *** addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical *** the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the *** analysis and security analysis illustrate the completeness and feasibility of the scheme.

关键词： Electronic health records cross-chain medical certificate blockchain data deletion and recovery

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Adaptive finite-time command-filtered backstepping sliding mode control for stabilization of a disturbed rotary-inverted-pendulum with experimental validation

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JVC/Journal of Vibration and Control 2023年第5-6期29卷 1431-1446页

作者： Mofid, Omid Alattas, Khalid A Mobayen, Saleh Vu, Mai The Bouteraa, Yassine Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia University of Sfax Sfax3038 Tunisia

In this paper, the finite-time stabilization of the disturbed and uncertain rotary-inverted-pendulum system is studied based on the adaptive backstepping sliding mode control procedure. For this purpose, first of all, the dynamical equation of the rotary-inverted-pendulum system is obtained in the state-space form in the existence of external disturbances and model uncertainties with unknown bound. Afterward, a novel command filter is defined to enhance the control strategy by consideration of a virtual control input. Therefore, the differential signal is replaced by the output of the command filter to reduce the complicated computing in the control process. Hence, the finite-time convergence of the sliding surface to the origin is attested by using the backstepping sliding mode control scheme according to the Lyapunov theory. Besides, the unknown upper bound of the exterior perturbation and uncertainty is approximated providing the adaptive control technique. Finally, simulations and experimental results are done to demonstrate the impression and proficiency of the suggested method. © The Author(s) 2022.

关键词： Inverted pendulum

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Wi-Fi Ranging under Interference

Wi-Fi Ranging under Interference

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2025 International Conference on Computing, Networking and Communications, ICNC 2025

作者： Zubow, Anatolij Laskos, Christos Rosler, Sascha Wunder, Gerhard Dressler, Falko Tu Berlin School of Electrical Engineering and Computer Science Germany Fu Berlin Cybersecurity and Ai Germany

ISBN: (纸本)9798331520960

The next generation of WiFi aims to deliver highly accurate location estimation through time-of-flight measurements. While initial results show promising performance, there is a lack of studies conducted under realistic, congested network conditions with significant cross-technology and co-channel interference. In this paper, we demonstrate that time of arrival (ToA)-based ranging, utilizing the MUSIC super-resolution algorithm, is severely impacted by interference. This is due to the fact that the channel state information (CSI) obtained in the presence of interference includes not only the characteristics of the channel but also the interference itself. This corrupted CSI leads to persistent ToA errors. Simulation results indicate that these errors are particularly pronounced in indoor scenarios with multipath propagation resulting in a ranging error of up-to 3 m which can only be mitigated by the usage of wider channels, e.g., 160 MHz, for ranging. © 2025 IEEE.

关键词： Cochannel interference

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Diff-IDS:A Network Intrusion Detection Model Based on Diffusion Model for Imbalanced Data Samples

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computers, Materials & Continua 2025年第3期82卷 4389-4408页

作者： Yue Yang Xiangyan Tang Zhaowu Liu Jieren Cheng Haozhe Fang Cunyi Zhang School of Cyberspace Security Hainan UniversityHaikou570228China Hainan Province Blockchain Technology Engineering Research Center Haikou570228China School of Computer Science and Technology Hainan UniversityHaikou570228China

With the rapid development of Internet of Things technology,the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast,bringing unprecedented challenges to the field of network security,especially in identifying malicious ***,due to the uneven distribution of network traffic data,particularly the imbalance between attack traffic and normal traffic,as well as the imbalance between minority class attacks and majority class attacks,traditional machine learning detection algorithms have significant limitations when dealing with sparse network traffic *** effectively tackle this challenge,we have designed a lightweight intrusion detection model based on diffusion mechanisms,named Diff-IDS,with the core objective of enhancing the model’s efficiency in parsing complex network traffic features,thereby significantly improving its detection speed and training *** model begins by finely filtering network traffic features and converting them into grayscale images,while also employing image-flipping techniques for data ***,these preprocessed images are fed into a diffusion model based on the Unet architecture for *** the model is trained,we fix the weights of the Unet network and propose a feature enhancement algorithm based on feature masking to further boost the model’s ***,we devise an end-to-end lightweight detection strategy to streamline the model,enabling efficient lightweight detection of imbalanced *** method has been subjected to multiple experimental tests on renowned network intrusion detection benchmarks,including CICIDS 2017,KDD 99,and *** experimental results indicate that Diff-IDS leads in terms of detection accuracy,training efficiency,and lightweight metrics compared to the current state-of-the-art models,demonstrating exceptional detection capabilities and robustness.

关键词： Network traffic feature enhancement diffusion model multi-classification Algorithm 2(continued)13:end for 14:Return y

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Towards Source-Free Domain Adaptive Semantic Segmentation Via Importance-Aware and Prototype-Contrast Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Cao, Yihong Zhang, Hui Lu, Xiao Xiao, Zheng Yang, Kailun Wang, Yaonan College of Computer Science and Electronic Engineering Hunan University Changsha China National Engineering Research Center of Robot Vision Perception and Control Technology School of Robotics Hunan University Changsha China College of Engineering and Design Hunan Normal University Changsha China

Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. IEEE

关键词： Semantics

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Leveraging Cross-Augmentation Consensus and Conflict for Semi-supervised Semantic Segmentation 27th

Leveraging Cross-Augmentation Consensus and Conflict for Se...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Junhao Chen, Junyi Huang, Sibo Zhang, Dongyu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Network and Information Center Huizhou University Huizhou China

ISBN: (纸本)9783031783975

Semi-supervised semantic segmentation leverages both labeled and unlabeled images to accomplish pixel-wise classification task. Within this field, the weak-to-strong consistency regularization has been widely popularized and has become a standard approach. However, unidirectional regularization often leads to the ignorance of correct but filtered predictions and brings the noise of wrong but confident predictions. To address these inherent flaws, we fully leverage Cross-Augmentation Consensus and Conflict (CACC), including Augmentation Feedback Mechanism (AFM) and Category Threshold Controller (CTC). AFM aims to mitigate the influence of incorrect predictions with high-confidence and mine unconfident but accurate predictions by re-weighting the pixel-wise pseudo supervision and applying supplementary regularization. Concurrently, CTC adopts category-specific thresholds by considering the model’s overall performance and the varying category-specific learning difficulty. Experimental results on benchmark datasets demonstrate the superior performance of our method, showcasing its effectiveness in improving semi-supervised semantic segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Counterfactually Augmented Event Matching for De-biased Temporal Sentence Grounding 24

Counterfactually Augmented Event Matching for De-biased Temp...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jiang, Xun Wei, Zhuoyuan Li, Shenshen Xu, Xing Song, Jingkuan Shen, Heng Tao Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Temporal Sentence Grounding (TSG), which aims to localize events in untrimmed videos with a given language query, has been widely studied in the last decades. However, recently researchers have demonstrated that previous approaches are severely limited in out-of-distribution generalization, thus proposing the De-biased TSG challenge which requires models to overcome weakness towards outlier test samples. In this paper, we design a novel framework, termed Counterfactually-Augmented Event Matching (CAEM), which incorporates counterfactual data augmentation to learn event-query joint representations to resist the training bias. Specifically, it consists of three components: (1) A Temporal Counterfactual Augmentation module that generates counterfactual video-text pairs by temporally delaying events in the untrimmed video, enhancing the model's capacity for counterfactual thinking. (2) An Event-Query Matching model that is used to learn joint representations and predict corresponding matching scores for each event candidate. (3) A Counterfact-Adaptive Framework (CAF) that incorporates the counterfactual consistency rules on the matching process of the same event-query pairs, furtherly mitigating the bias learned from training sets. We conduct thorough experiments on two widely used DTSG datasets, i.e., Charades-CD and ActivityNet-CD, to evaluate our proposed CAEM method. Extensive experimental results show our proposed CAEM method outperforms recent state-of-the-art methods on all datasets. Our implementation code is available at https://***/CFM-MSG/CAEM-Code. © 2024 ACM.

关键词： Spatio-temporal data

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A Review on Wideband High-Gain Low-THz Antennas for Wireless Applications 18

A Review on Wideband High-Gain Low-THz Antennas for Wireless...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Srinivasan, Dhamodharan Premkumar, M. Deepa Nivethika, S. Dhilipkumar, P. Parameswari, S. Chowdary, M. Kalpana Centre for Blockchain and Cybersecurity Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India Ssm Institute of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Vit University School of Computer Science and Engineering Chennai India Sri Ranganathar Institute of Engineering and Technology Department of Ece Coimbatore India Sri Sairam Insitute of Technology Department of Ece Chennai India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544519

In this article, the potential of low-terahertz (THz) technology is discussed to present high data rates in future biomedical systems, and also in the 6G mobile system. However, due to the loss, the design of high-gain antennas is crucial to overcome power limitations and compensate for the additional path loss. This article highlights recent developments in wideband and high-gain sub-mm-wave and low-THz antennas and their fabrication technologies. The advancements in recent technologies have enabled the manufacture of antennas with complicated structures with precise accuracy and less expensive. Overall, the paper emphasizes the importance of high-gain antennas in overcoming the challenges associated with low-THz technology and highlights the potential of AM technology in the development of low-cost, high-performance THz antennas. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Antennas high gain low-cost low-terahertz three-dimensional printing (3DP)

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Stacked Regressor for Crop Yield Prediction 1

Stacked Regressor for Crop Yield Prediction

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Gupta, Harsh Vincent, Rajiv Rajakumaran, Gayathri Sivaraman, Arun Kumar Rajesh, M. Sultana, Ajmery School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Solution Center Photon Interactive Inc ONL6T 4A8 Canada School of Computer Science and Technology Algoma University ON Canada

ISBN: (纸本)9798350365337

A significant number of people in developing nations like India, when a primary source of income is farming, depend on the agriculture sector for their economy. The agricultural sector can be improved to make it more effective and lucrative for farmers by harnessing the prospects of data and analytics. This work suggests using deep learning and machine learning techniques to forecast crop yield throughout India. The work trains the models using a variety of input variables, including weather information, soil characteristics and crop character traits. The findings demonstrate that models based on machine learning and deep learning are effective at accurately predicting crop yield. Intelligent choices about crop production and management can be made using the proposed approach by farmers and policymakers. Using a stacked ensemble model of Random Forest, Gradient Boosting, Bagging regressor and RNN as the meta-learner layer, this work evaluates crop yield. © 2024 IEEE.

关键词： Recurrent neural networks

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Enhancing Micro-Expression Recognition with Broadbent Attention Mechanism: A High-Performance Approach for Emotion Detection 21

Enhancing Micro-Expression Recognition with Broadbent Attent...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ashraf, Imran Kehkashan, Tanzila Zaman, Muhammad Akhunzada, Adnan Talib, Hafiz Khizer Bin Cao, Yanlong University of Lahore Faculty of Computer Science Lahore Pakistan Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia COMSATS University Islamabad Department of Computer Science Islamabad Pakistan University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350378078

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and Stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with Stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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