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Research on Blockchain: Privacy Protection of Cryptography Blockchain-Based Applications 3

Research on Blockchain: Privacy Protection of Cryptography B...

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3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Dayong, Zhang Wahab, Nur Haliza Abdul Kadir, Khairunnisa A. Aldhaqm, Arafat Nasir, Haslinah Mohd Wong, Keng Yinn Universiti Teknologi Malaysia Faculty of Computing Johor Malaysia Universiti Teknikal Malaysia Melaka Electronics & Computer Engineering Technology Department Durian Tunggal Melaka Malaysia Universiti Teknologi Malaysa Faculty of Mechanical Johor Malaysia

ISBN: (纸本)9798350305333

Blockchain is a distributed database technology developed because of cryptocurrency, which has the characteristics of open and transparent ledger data. However, attackers will analyze ledger data to obtain sensitive information, threatening transaction privacy of users. Cryptography is the underlying technology of the blockchain to ensure the integrity and legitimacy of transactions in the blockchain. Providing privacy protection for the blockchain by studying cryptography has become a research hotspot in the blockchain. This paper introduces the cryptography technology in the blockchain, summarizes the privacy protection problems existing in the current blockchain, and expounds the existing cryptography solutions and the advantages and disadvantages of each scheme. Finally, the future research directions of cryptography in blockchain are summarized and prospected. © 2023 IEEE.

关键词： Blockchain

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Research and optimization of intra-node communication operator performance based on domestic heterogeneous platforms 3

Research and optimization of intra-node communication operat...

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3rd international conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Ming, Zhe Ding, Guangyu Han, Mengzhi Zhengzhou University No.100 Science Road Henan Zhengzhou China Co. Ltd No.36 Zhongguancun Software Park No.8 Dongbeiwang West Road Haidian District Beijing China

ISBN: (纸本)9781510680449

With the increasing demand for computing power in machine learning tasks, the training of deep neural network models has been pushed to multi-GPU training or even larger scale distributed training. However, the acceleration effect and scalability of model training are largely limited by the communication efficiency between GPUs. In order to improve the communication efficiency of domestic GPU accelerator, this paper studies and analyzes the communication performance of Allreduce operator widely used in deep learning tasks. Based on a data compression algorithm and multi-stream parallel technology, this paper ports and optimizes the Allreduce operator on domestic heterogeneous platform. The experimental data results show that compared with RCCL, the ported optimized version of Allreduce operator achieves different degrees of performance improvement such as 30% to 90% under different data sizes. This paper implements the transplantation and optimization of Allreduce operator on the domestic accelerated heterogeneous platform, and achieves good acceleration results, which provides support for the efficient communication of domestic GPU accelerators and the ecological construction of domestic heterogeneous platforms © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Graphics processing unit

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Reference-Free Isotropic 3D EM Reconstruction Using Diffusion Models 1

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3rd Workshop on Deep Generative Models for Medical Image computing and Computer Assisted Intervention (DGM4MICCAI) at the 26th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Lee, Kyungryun Jeong, Won-Ki Korea Univ Dept Comp Sci & Engn Coll Informat Seoul South Korea

ISBN: (数字)9783031537677

ISBN: (纸本)9783031537660;9783031537677

Electron microscopy (EM) images exhibit anisotropic axial resolution due to the characteristics inherent to the imaging modality, presenting challenges in analysis and downstream tasks. Recently proposed deep-learning-based isotropic reconstruction methods have addressed this issue;however, training the deep neural networks require either isotropic ground truth volumes, prior knowledge of the degradation process, or point spread function (PSF). Moreover, these methods struggle to generate realistic volumes when confronted with high scaling factors (e.g. x8, x10). In this paper, we propose a diffusion-model-based framework that overcomes the limitations of requiring reference data or prior knowledge about the degradation process. Our approach utilizes 2D diffusion models to consistently reconstruct 3D volumes and is well-suited for highly downsampled data. Extensive experiments conducted on two public datasets demonstrate the robustness and superiority of leveraging the generative prior compared to supervised learning methods. Additionally, we demonstrate our method's feasibility for self-supervised reconstruction, which can restore a single anisotropic volume without any training data. The source code is available on GitHub: https://***/hvcl/diffusion-em-recon.

关键词： Diffusion models Isotropic EM reconstruction Super-Resolution

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Quantum neural network quantum state 3

Quantum neural network quantum state

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Pang, Xin Li, Yanan Wang, Zhimin Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Quantum many-body computation is a central challenge across multiple domains of scientific research, and Monte Carlo method has emerged as one of the most effective numerical techniques for investigating large-scale many-body systems. In recent years, the integration of Monte Carlo methods with machine learning has led to the development of neural quantum state approach. Quantum neural networks (QNNs) offer superior expressiveness compared to classical neural networks. In this paper, we propose a novel Quantum Neural Network Quantum State (QNNQS) method, based on the variational Monte Carlo framework. This approach leverages a quantum neural network to represent the many-body quantum state, and optimizes it iteratively through gradient descent to determine the ground-state energy. To assess the performance of this method, we conduct simulations of the one-dimensional transverse-field Ising model, investigating the influence of quantum circuit architecture on circuit expressibility, entangling capability, and computational accuracy. The results demonstrate that the QNNQS method achieves comparable simulation accuracy while utilizing significantly fewer parameters and requiring less training resources than classical network quantum state methods. © 2025 SPIE.

关键词： Quantum computers

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Design of ceiling light with air quality monitoring and heat dissipation structure based on duct technology 3

Design of ceiling light with air quality monitoring and heat...

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3rd international conference on Electronic Information Engineering and data Processing, EIEDP 2024

作者： Yu, Liang Mo, Jiayu Lu, Jiancong Lai, Yangbing Tian, Liwei College of Computer Science Guangdong University of Science and Technology AIoT Edge Computing Engineering Technology Research Center Dongguan523000 China

ISBN: (纸本)9781510680531

Most air quality monitors and lighting fixtures are currently separate, with air quality monitors typically being stand-alone devices that require separate power supplies and mounting locations. This not only occupies valuable space but also complicates their use and management. The emergence of air quality monitoring ceiling lights in smart homes aims to address this issue. However, many existing ceiling light designs do not consider heat dissipation, which can lead to performance issues, reduced lifespan, and even fire hazards. To tackle these challenges, this study presents the design of an intelligent ceiling lamp for air quality monitoring utilizing air duct technology. Equipped with a seven-in-one air quality sensor, the ceiling lamp collects indoor air data and performs real-time intelligent control and anomaly identification through a cloud-side-end synergy approach. The incorporation of air ducts enhances the sensitivity and accuracy of air quality monitoring, facilitates thermal convection, and improves heat dissipation efficiency, ultimately enhancing performance and longevity. Physical product design and field tests conducted on the cloud-side-end platform demonstrate that the ceiling lamp's air quality monitoring and anomaly identification have an average inference time of less than 1 millisecond with an identification accuracy of over 98%. Furthermore, the air duct design improves thermal efficiency by approximately 28%. Overall, this intelligent ceiling lamp represents a practical, efficient, and intelligent multi-functional indoor lighting solution. © 2024 SPIE.

关键词： Efficiency

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Remote sensing identification of seasonal pasture based on Sentinel-2 23

Remote sensing identification of seasonal pasture based on S...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Su, Shiqi Fang, Panfei Qian, Rui Zheng, Pengfei Qiang, Zhenping College of Big Data and Intelligent Engineering Southwest Forestry University Swfu Yunnan Kunming China Southwest Forestry University Swfu Faculty of Forestry Yunnan Kunming China

ISBN: (纸本)9781665494571

In order to research the distribution of seasonal pastures, we used the Sentinel-2 remote sensing data of Golog Tibetan Autonomous Prefecture in 2017-2020 warm season (270-300d) to construct 22 spectral features and topographic features based on GEE platform. Random forest classifier was used to identify seasonal pastures. The results showed that: (1) the overall accuracy and Kappa coefficient of seasonal pasture identification were 82.41% and 0.74, respectively;The producer's accuracy and user's accuracy were (cold season pasture: 84.08%, 80.57%;Warm season pasture: 71.92%, 75.26%);(2) The total area was about 7320972.81 hm2, including 3080067.88 hm2of cold-season pasture and 4240904.93 hm2of warm season pasture. The cold season pasture was mainly distributed in Banma, Jiuzhi and Maqin counties, while the cold-season pasture was mainly distributed among Maduo, Dari and Gande counties. (3) The imagery of the end of the warm season (270-300d) during 2017-2020 were synthesized to ensure the integrity of the image of the study area as much as possible and eliminate the influence of clouds;(4) The water loss and surface exposure to herbage after grazing and treading by livestock are similar to the carbonized surface after fire. Therefore, the spectral characteristics of NDVI and NDMI which sensitive to water and CSI, NBR and NBR2 which sensitive to bare surface have good effects on the identification of seasonal pasture. (5) Most of the areas with high altitude belong to other types, indicating that elevation factors perform well in distinguishing seasonal pastures from other types of land. The results of this study can provide a reference to using remote sensing images to quickly identify seasonal pasture distribution and formulate scientific grazing management policies. © 2021 IEEE.

关键词： Water Smart cities Time series analysis Vegetation mapping Land surface Fires Agriculture

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Identification of Rice Species and Adulteration Using Gas Chromatography-ion Mobility Spectrometry and Multi-classification Maximum-interval Twin Support Vector Machine 3

Identification of Rice Species and Adulteration Using Gas Ch...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Lian, Feiyu Fu, Maixia College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Gas chromatography-ion mobility spectrometry (GC-IMS) was applied to identify rice varieties and adulteration in order to address the inefficiencies caused by the dependence on intricate biochemical procedures for the identification of rice species and *** original migration profile data for the identification of rice varieties and adulteration were provided by testing the volatile flavor substances of 5 kinds of rice. The original data was compressed through principal component analysis (PCA), which was then used as the classifier's input. An improved multi-classification maximum-interval twin support vector machine (MMTSVM) was proposed to construct a multi-classifier with increased classification efficiency and accuracy compared to the conventional support vector machine. The experimental results on the testing set demonstrated that the accuracy of rice variety identification was close to 94.00%, which was obviously better than that of conventional support vector machines, decision trees, and other models. At the same time, the experimental results also showed that the accuracy of the GC-IMS method inidentifying adulterated rice reached 93.3%, outperforming the traditional chromatographic or spectral analysis methods. © 2023 SPIE.

关键词： Principal component analysis

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Research on Intelligent Algorithm for Autonomous Landing of Quadrotor UAV 3

Research on Intelligent Algorithm for Autonomous Landing of ...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Xue, Chaohang Jin, Liying Hu, Kelian Hu, Xindong Shen, Yujie Xiangyang Key Laboratory of Intelligent Manufacturing and Machine Vision Hubei University of Arts and Science Hubei Province Xiangyang441053 China AVIC Aerospace Life-Support Industries Ltd. 441003 China Hubei Xiangyi New Material Technology Co. Ltd. 441003 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

This paper takes the autonomous landing of four-rotor UAV as the specific research object. Aiming at the problem of identifying the feature points of landing signs and improving the accuracy of autonomous landing, an improved multi-objective soft subspace clustering algorithm is proposed. The anti-redundant mutation operator and forward comparison operation are designed, it is proposed to improve the population diversity and convergence speed of the algorithm. The iNSGA-II is used as the base algorithm, and the repair operator and local search operator are designed to reflect the clustering characteristics. An autonomous landing control model based on visual recognition is established. A large number of position feature points are clustered and analyzed to decouple the highly nonlinear mapping relationship in the feature space, and then the redundant information between the dimensions is stripped to achieve the maximum stability of the sampling points, the importance sequence of all features and the accurate classification. © 2023 SPIE.

关键词： Clustering algorithms

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Occluded Face Recognition with Deep Learning 3rd

Occluded Face Recognition with Deep Learning

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3rd international conference on computing and data science, conf-cds 2021

作者： Jiayu, Qin School of Information Science and Technology Fudan University Shanghai China

ISBN: (纸本)9789811688843

Face recognition is an research topic of great importance in both the academia and industry, with a large number of applications based on this technology. Occluded face recognition is even more challenging when the whole face is not available. We find this problem has not been completely solved and is still a hot topic in recent years. Based on a real-world masked face dataset, we conduct a series of experiments with an aim of evaluating five advanced deep learning models and find that DenseNet performs the best with a test accuracy of 0.8012. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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The Impact of Parameters on Semantic Segmentation: A Case Study on the CamVid dataset 23

The Impact of Parameters on Semantic Segmentation: A Case St...

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作者： Zhang, Yue Yang, Ruiqi Wang, Jun Chen, Nan Dai, Qinling Southwest Forestry University Swfu Faculty of Forestry Yunnan Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Swfu Yunnan Kunming China Art and Design College Southwest Forestry University Swfu Yunnan Kunming China

ISBN: (纸本)9781665494571

Image segmentation is an important topic of remote sensing image processing. High-resolution remote sensing images contain complex object information. The application of traditional segmentation methods is greatly restricted. Deep learning is suitable for processing remote sensing images due to the complex multi-layer nonlinear transformation characteristics. Many image semantic segmentation methods based on deep learning were proposed, which realizes an end-to-end semantic segmentation workflow. Most of those studies focused on the design of network architectures. In contrast, the impact of parameters on final segmentation accuracy was not fully exploited. In this context, with the CamVid dataset and popular U-Net convolutional neural network and DeeplabV3+, this paper explored the impact of the max Epoch and the initial learning rate on the segmentation results. The study results show that in Deeplab V3+, when the MaxEpochs parameters are different and the InitialLearningRate parameters are the same, the improvement of Global Accuracy is not obvious. When the InitialLearningRate parameters are different and the MaxEpochs parameters are the same, the Global Accuracy has been greatly improved, increasing by about 3%. In U-Net, when the MaxEpochs parameters are different and the InitialLearningRate parameters are the same, the Global Accuracy is improved by about 1%. When the InitialLearningRate parameters are different and the MaxEpochs parameters are the same, the Global Accuracy is greatly improved, increasing by 24%. They can be concluded that the max epochs have slight impact on the final global accuracy, in contrast, the InitialLearningRate has a great impact on the experimental results. © 2021 IEEE.

关键词： Deep learning Image segmentation Smart cities Semantics Network architecture Convolutional neural networks Remote sensing

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