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Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Hussain, Altaf Akbar, Wajahat Hussain, Tariq Bashir, Ali Kashif Dabel, Maryam M. Al Ali, Farman Yang, Bailin School of Electronic and Control Engineering Chang’an University Xi’an China School of Computer Science and Technology and School of Mathematics and Statistics Zhejiang Gongshang University Hangzhou China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%. IEEE

关键词： Anonymity

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CLASS-SPECIFIC DISTRIBUTION ALIGNMENT FOR SEMI-SUPERVISED MEDICAL IMAGE CLASSIFICATION

arXiv

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arXiv 2023年

作者： Huang, Zhongzheng Wu, Jiawei Wang, Tao Li, Zuoyong Ioannou, Anastasia College of Computer and Control Engineering Minjiang University China College of Computer and Data Science Fuzhou University China College of Electrical and Mechanical Engineering Fujian Agriculture and Forestry University China International Digital Economy College Minjiang University China Department of Computer Science and Engineering European University Cyprus Cyprus Cyprus

Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases. To address this problem, we propose Class-Specific Distribution Alignment (CSDA), a semi-supervised learning framework based on self-training that is suitable to learn from highly imbalanced datasets. Specifically, we first provide a new perspective to distribution alignment by considering the process as a change of basis in the vector space spanned by marginal predictions, and then derive CSDA to capture class-dependent marginal predictions on both labeled and unlabeled data, in order to avoid the bias towards majority classes. Furthermore, we propose a Variable Condition Queue (VCQ) module to maintain a proportionately balanced number of unlabeled samples for each class. Experiments on three public datasets HAM10000, CheXpert and Kvasir show that our method provides competitive performance on semi-supervised skin disease, thoracic disease, and endoscopic image classification tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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Mixed-Integer Optimal control With Disjunctive Characteristic Time Constraints for Energy-Optimal Speed Planning

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IEEE Transactions on Transportation Electrification 2025年

作者： Dong, Shiying Chen, Hong Luo, Xi Meng, Xiangyu Huang, Yanjun Liu, Qifang Gao, Bingzhao Jilin University Department of Control Science and Engineering Changchun China Tongji University College of Automotive Studies Shanghai China Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States

This paper considers the energy optimization problem with multiple time window constraints, which is motivated by the eco-driving of connected and automated vehicles (CAVs) in urban traffic. Timely crossing of signalized intersections defines complex spatio-temporal constraints, and multiple available open time windows introduce the discontinuous feasible domains of crossing times. The CAVs should select an open time-window for each intersection, determine the crossing times, and optimize the speed profiles accordingly, which turns out to be a hard-to-solve optimization problem. To address this challenge, this paper proposes a unifying approach that transforms the energy optimization problem into a novel mixed-integer optimal control problem with characteristic time constraints (MIOCP-CT). This approach facilitates the simultaneous optimization of open time windows, crossing times, and speed profiles within a single optimization problem, which can be solved by our proposed novel numerical solution procedure. The application of the proposed method in the eco-driving of CAVs approaching multiple signalized intersections demonstrates its performance within real-world urban driving cycles. The experimental results of the hardware-in-the-loop (HiL) demonstrate a superior energy-saving performance of 5.83% compared to existing methods, while also achieving real-time feasibility within 100 milliseconds. © 2015 IEEE.

关键词： Mixed-integer linear programming

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A Novel Conv-Mamba-Hybrid Network for Medical Image Segmentation

A Novel Conv-Mamba-Hybrid Network for Medical Image Segmenta...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Sibo Qiao Zhiyuan Zhao Pengfei Xie Wenjing Yin Min Wang Yuanyuan Zhang Shanchen Pang The College of Software Tiangong University Tianjin China The College of Computer Science and Technology China University of Petroleum Qingdao China The Department of Electrical Engineering and Computer Science Khalifa University of Science and Technology Abu Dhabi United Arab Emirates The College of Life Sciences Tiangong University Tianjin China The College of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In the rapidly advancing landscape of intelligent medical technology, high-precision and widely applicable medical image segmentation techniques are pivotal in advancing personalized treatment plans and enhancing patient experiences. To address inherent obstacles, such as complex structures and features that are difficult to capture in segmentation tasks, we first propose a CMH module that merges convolutional neural networks (CNNs) with state space models (e.g., Mamba). This module hierarchically integrates CNN’s local priors into the Mamba layer, adeptly capturing specific details of target objects and broader contextual information from different perspectives. Subsequently, we introduce an information fusion module based on Mamba (MIH), aimed at dynamically fusing feature information from different paths and levels to enhance feature expression and discrimination. Building upon the abovementioned modules, we develop an unexplored segmentation model, CMHNet, capable of flexibly capturing feature maps of varying scales and angles from medical images. In the experiments, we present CMHNet’s distinguished performance in segmentation tasks through massive experiments on four medical datasets.

关键词： Image segmentation Sensitivity Anatomical structure Medical services Robustness Real-time systems Lesions Medical diagnostic imaging Optimization Testing

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FlowTracker: A Refined and Versatile Data Plane Measurement Approach

SSRN

引用

SSRN 2024年

作者： Jiang, Xinyue Wu, Chunming Zhou, Zhengyan Wang, Di Kong, Dezhang Khurram Khan, Muhamma Liu, Xuan College of Computer Science and Technology Zhejiang University Hangzhou310027 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China Center of Excellence in Information Assurance King Saud University Riyadh11362 Saudi Arabia College of Information Engineering College of Artificial Intelligence Yangzhou University Yangzhou225009 China School of Computer Science and Engineering Southeast University Nanjing210023 China

To acquire per-hop flow-level information, existing works have made significant contributions to offloading network measurement onto data center switches. Despite this, they still pose challenges due to increasingly complex measurement tasks and massive network traffic. In this paper, we introduce FlowTracker, a flow measurement primitive in the data plane. Our key innovation is a hash-based data structure with constant size and collision resolution, which allows fine-grained and real-time monitoring of various flow statistics. We have fully implemented a FlowTracker prototype on a testbed and used real-world packet traces to evaluate its performance. The results demonstrate FlowTracker’s efficiency under different measurement tasks. In addition, FlowTracker requires low overhead, as well as ∼0.04% extra bandwidth for processing in large-scale networks. With FlowTracker, operators can gain a deeper understanding of their networks and make more accurate flow control decisions. © 2024, The Authors. All rights reserved.

关键词： Flow measurement

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A Survey of fMRI to Image Reconstruction

arXiv

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arXiv 2025年

作者： Guo, Weiyu Sun, Guoying He, Jianxiang Shao, Tong Wang, Shaoguang Chen, Ziyang Hong, Meisheng Sun, Ying Xiong, Hui Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China College of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Control Science and Engineering Shandong University China

Functional magnetic resonance imaging (fMRI)based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and large-scale datasets have driven progress, challenges such as data scarcity, cross-subject variability, and low semantic consistency persist. To address these issues, we introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field. This review highlights key challenges, categorizes methodologies such as fMRI signal encoding, feature mapping, and image generator. Finally, promising research directions are proposed to advance this emerging frontier, providing a reference for future studies. © 2025, CC BY.

关键词： Image reconstruction

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Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

引用

engineering 2023年第1期20卷 77-95页

作者： Jun Liu Kangli Dong Yi Sun Ioannis Kakkos Fan Huang Guozheng Wang Peng Qi Xing Chen Delin Zhang Anastasios Bezerianos Yu Sun Key Laboratory for Biomedical Engineering of Ministry of Education of China Zhejiang UniversityHangzhou 310007China Department of Neurology Sir Run Run Shaw HospitalSchool of MedicineZhejiang UniversityHangzhou 310020China School of Electrical and Computer Engineering National Technical University of AthensAthens 15780Greece Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 200092China Department of Anesthesiology The First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China The N1 Institute for Health Center for Life SciencesNational University of SingaporeSingapore 117456Singapore

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

关键词： Anesthesia Brain network Connectivity Graph theoretical analysis Clinical monitoring system

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Rethinking guidance information to utilize unlabeled samples: a label encoding perspective 24

Rethinking guidance information to utilize unlabeled samples...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Yulong Zhang Yuan Yao Shuhao Chen Pengrong Jin Yu Zhang Jian Jin Jiangang Lu Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China and College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China College of Control Science and Engineering Zhejiang University Hangzhou China

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning. However, EntMin emphasizes prediction discriminability while neglecting prediction diversity. To alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled samples. By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding. Inspired by this finding, we propose a Label-Encoding Risk Minimization (LERM). It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label encodings. As a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a plugin. Theoretically, we analyze the relationships between LERM and ERM as well as EntMin. Empirically, we verify the superiority of the LERM under several label insufficient scenarios. The codes are available at https://***/zhangyl660/LERM.

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