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VSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain

ACM Transactions on Sensor Networks 2024年第2期20卷 1-22页

作者： Tian, Siben Bai, Fenhua Shen, Tao Zhang, Chi Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Applications China Faculty of Information Technology Beijing University of Technology China

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm's complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis techniques: image processing Sun: fundamental parameters

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Machine Learning-Based Survival Prediction Model for Melanoma 24

Machine Learning-Based Survival Prediction Model for Melanom...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Jing, Zijie Wang, Jianghong Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China

ISBN: (纸本)9798400717826

The high growth rate of melanoma poses a huge challenge to healthcare delivery worldwide. At this stage, rapid and accurate diagnosis and timely treatment of melanoma is crucial. In this study, we utilized multiple metrics to predict survival in melanoma patients. We built multiple models of different types of machine learning, deep learning, and integrated learning, of which the best results were Logistic regression, Long short-term memory, and Categorical boosting, with AUCs of 0.9267, 0.9365, and 0.9417, respectively. We performed interpretability analyses of these models to improve the models’ applicability in the clinic. The conclusions obtained from the analysis have important implications for the treatment of melanoma. This study represents a significant advancement in the use of machine learning methods to predict the survival status of individuals with melanoma, providing a new way of thinking about melanoma treatment and prognosis. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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A Secure and Efficient Privacy Scheme for Location-Based Services in Cloud Environments 24

A Secure and Efficient Privacy Scheme for Location-Based Ser...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Pu, Tongyu Zeng, Kai Bai, Fenhua Zhang, Chi Zhang, Xiaohui Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China

ISBN: (纸本)9798400717161

Preserving privacy has become crucial in the field of location-based services (LBS) to leverage their full potential while protecting users from untrustworthy LBS providers. Given that LBS users typically use resource-constrained mobile devices, many existing privacy-preserving methods focus on anonymization. However, these methods have certain limitations regarding privacy protection and efficiency. This paper introduces a novel Privacy-preserving LBS scheme to address these issues. We develop an efficient query mechanism where users send encrypted distance queries to the server. The server then employs a PFR-tree and homomorphic encryption to filter Points of Interest (POIs) in a hierarchical manner. The PFR-tree quickly eliminates irrelevant regions based on their locations, minimizing unnecessary computations and enhancing query efficiency. Experimental results indicate that this PFR-tree-based scheme significantly reduces query time compared to traditional methods without a PFR tree, especially as the number of POIs increases, demonstrating superior performance and efficiency in scenarios with many POIs. This approach effectively meets privacy protection needs for indoor navigation in cloud environments while maintaining high efficiency and security. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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A Columnar Storage Cone Search Method for Massive Astronomical Data 5

A Columnar Storage Cone Search Method for Massive Astronomic...

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5th International Conference on information science, Parallel and Distributed Systems, ISPDS 2024

作者： Liu, Yu-Hai Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798350352719

In recent years, with the continuous advancement of astronomical instrument manufacturing technology, astronomical observation data has shown an exponential growth trend, and traditional database technologies are no longer able to meet the storage and retrieval performance requirements. To address the challenges of storing massive astronomical data and efficient cone search, a solution utilizing columnar storage is proposed, leveraging the separate storage of each attribute column in columnar storage for independent retrieval to enhance data read efficiency. Building upon this, a specialized cone search area retrieval method tailored for columnar storage mode is introduced to reduce data read and computation in cone search, further enhancing retrieval efficiency. © 2024 IEEE.

关键词： Storage efficiency

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Adversarial dual decision-based model for event-related opinion sentence recognition

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Multimedia Tools and Applications 2024年 1-18页

作者： Xiang, Yan Qin, Yuan Yu, Zhengtao Huang, Yuxin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

Event-related opinion sentences recognition aims to mine the valuable comments discussing about the specific event from the mass of microblogs comments. It’s difficult to label sufficient comments for a new microblogs event, and domain adaptation methods are suitable for this task, which can transfer the knowledge from the source domain(labeled event) to the target domain (unlabeled event). However, existing domain adaptation models do not make full use of the domain invariance to realize categories alignments of the two domains. Therefore, we propose a adversarial dual decision model for opinion sentences recognition, which takes advantage of the domain invariance that the opinion sentences are correlated with the event description. The proposed model has two generators to obtain different event-correlated features, and two classifiers to make dual decisions based on the two features. Through the adversarial process of generators and classifiers, the model completes the category feature alignment and realizes the opinion sentences recognition for the new event. The comparative experiment results show that our model outperforms the state-of-the-art models on six opinion sentences recognition tasks. In addition, we verify the effectiveness of each component of our model by ablation experiments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

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Research on MEC computing offload strategy for joint optimization of delay and energy consumption

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Mathematical Biosciences and engineering 2024年第6期21卷 6336-6358页

作者： Ni, Mingchang Zhang, Guo Yang, Qi Yin, Liqiong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China Kunming Iron & Steel Holding Co. Ltd. Kunming650302 China

The decision-making process for computational offloading is a critical aspect of mobile edge computing, and various offloading decision strategies are strongly linked to the calculated latency and energy consumption of the mobile edge computing system. This paper proposes an offloading scheme based on an enhanced sine-cosine optimization algorithm (SCAGA) designed for the "edge-end" architecture scenario within edge computing. The research presented in this paper covers the following aspects: (1) Establishment of computational resource allocation models and computational cost models for edge computing scenarios;(2) Introduction of an enhanced sine and cosine optimization algorithm built upon the principles of Levy flight strategy sine and cosine optimization algorithms, incorporating concepts from roulette wheel selection and gene mutation commonly found in genetic algorithms;(3) Execution of simulation experiments to evaluate the SCAGA-based offloading scheme, demonstrating its ability to effectively reduce system latency and optimize offloading utility. Comparative experiments also highlight improvements in system latency, mobile user energy consumption, and offloading utility when compared to alternative offloading schemes. © 2024 the Author(s).

关键词： Energy utilization

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A Study and Analysis of Predicting College Students’ Final Exam Scores by Integrating Physical Fitness Test Data and Poverty Level information 5th

A Study and Analysis of Predicting College Students’ Final ...

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5th International Conference on Computer science and Educational Informatization, CSEI 2023

作者： Xu, Nuo Zhang, Xiaoli Zhu, Guifu Wen, Jun Nie, Jialei Yang, Can Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Information Technology Center Kunming University of Science and Technology Kunming China

ISBN: (纸本)9789819994915

To accurately predict the final exam scores of college students, a prediction model based on random forest is constructed by collecting data that may affect the final exam scores from multiplatform databases. The performance of this model is compared with the traditional neural networks and support vector machine model. At the same time, to further optimize the model, a feature selection method based on the random forest is proposed. The number of input parameters was increased or decreased in turn to establish a new model. The contribution of each input parameter to the overall model was calculated through the changes in the accuracy of the models before and after. The parameters required for modelling are selected according to the contribution of each parameter;in this manner, the model is simplified. The results show that the random forest model is better in fitting ability, generalization and fitting accuracy than the traditional neural networks and support vector machine. Feature selection can effectively simplify the model and improve prediction accuracy. Through the verification on the data of students in another grade, the model is universal. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Simultaneous Detection of Flare-related Decaying and Decayless Kink Oscillations Using Jerk-aware Motion Magnification

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Research in Astronomy and Astrophysics 2022年第11期22卷 130-136页

作者： Xiaowei Guo Bo Liang Song Feng Wei Dai Yunfei Yang Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China

Kink oscillations of coronal loops are often influenced by external events and this results in various changes of the *** the changes can provide valuable information for understanding kink *** observation focuses on a flare region acquired by the Atmospheric Imaging Assembly on the Solar Dynamics Observatory spacecraft on 2016 March *** are a bunch of arched loops and an open loop near the ***,their oscillations show very low *** we used the jerk-aware motion method to magnify the weak *** found that before the flare onset at 02:59 UT,there were some large loops above the arched loops being raised *** properties of the weak oscillations show clear *** the one hand,the oscillations in the arched loops were decayless initially,but both their amplitude and period increased after 02:30 UT and before the flare *** enhanced,the oscillations decayed with *** the other hand,the oscillations in the open loop were nearly constant before and after 02:30 UT,but their period *** means that the changes in periods and amplitudes of oscillations are likely associated with the loops raised before the flare.

关键词： Sun:corona Sun:oscillations Sun:flares waves

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MSF-TransUNet: A Multi-Scale Fusion Approach for Precise Cardiac Image Segmentation 24

MSF-TransUNet: A Multi-Scale Fusion Approach for Precise Car...

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4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Zhang, Nuorui Li, Jingtao Faculty of Information Engineering and Automation Kunming University of Science & Technology Kunming Yunnan China

ISBN: (纸本)9798400710247

Cardiac image segmentation is crucial in the detection and treatment of cardiovascular disorders. The complexity of cardiac anatomy, morphological variations under different pathological conditions, and inconsistencies in imaging quality pose significant challenges to existing medical image segmentation methods. In order to tackle these difficulties, this work presents the MSF-TransUNet, an innovative structure specifically developed for the purpose of segmenting cardiac images. This model harnesses the synergistic capabilities of Convolutional Neural Networks (CNNs) and Transformer architectures to tackle the intricacies of cardiac anatomy and variations in imaging conditions. The MSF-TransUNet incorporates an Efficient Pyramid Squeeze Attention Network (EPSANet) as its encoder, enhancing multi-scale feature representation and long-range channel dependencies. Additionally, a Multi-Scale Fusion Module (MSFM) is developed to integrate multi-scale features from various encoder layers within the skip connections, utilizing convolution operations and the SCSE attention mechanism for efficient feature fusion. To overcome the challenges of cardiac structural complexity and data imbalance, the model introduces an Adaptive t-vMF Dice Loss, dynamically adjusting similarity metrics to enhance segmentation performance across different categories and regions. Extensive evaluations on two widely used cardiac image segmentation datasets, CAMUS and ACDC, demonstrate that the MSF-TransUNet outperforms existing state-of-the-art models on several metrics. Notably, our model achieves average Dice coefficients of 0.9243 and 0.9206, showcasing its flexibility and accuracy in handling scenarios with poor image quality or complex cardiac anatomy. © 2024 Copyright held by the owner/author(s).

关键词： Image segmentation

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