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Survey on the Improvement and Application of HHL Algorithm

Survey on the Improvement and Application of HHL Algorithm

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2022 international conference on Computer Modelling and big data Analytics, CMBDA 2022

作者： Liu, Xiaonan Xie, Haoshan Liu, Zhengyu Zhao, Chenyan State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University ZhengZhou450000 China School of Computer and Artificial Intelligence ZhengZhou University ZhengZhou450000 China

Quantum computing is a new computing mode that follows the laws of quantum mechanics to control quantum information units for computation. In terms of computational efficiency, due to the existence of quantum mechanical superposition, some known quantum algorithms can process problems faster than traditional general-purpose computers. HHL algorithm is an algorithm for solving linear system problems. Compared with classical algorithms in solving linear equations, it has an exponential acceleration effect in certain cases and as a sub-module, it is widely used in some machine learning algorithms to form quantum machines learning algorithms. However, there are some limiting factors in the use of this algorithm, which affect the overall effect of the algorithm. How to improve it to make the algorithm perform better has become an important issue in the field of quantum computing. This paper summarizes the optimization and improvement of HHL algorithm since it was proposed, and the application of HHL algorithm in machine learning, and discusses some possible future improvements of some subroutines in HHL algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Linear systems

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Design and Implementation of Wastewater Pollutant data Analysis and Visualization System for Vessels 4

Design and Implementation of Wastewater Pollutant Data Analy...

引用

2022 4th international conference on Artificial intelligence Technologies and Applications, ICAITA 2022

作者： Lv, Taizhi Tang, Peiyi Zou, Yujuan College of Information Technology Jiangsu Maritime Institute Nanjing China General Manager Office Nanjing Huihai Transportation Technology Company Limited Nanjing China

To monitor the discharge of water pollutants from vessels, a monitoring system for the discharge of water pollutants from vessels has been established on the Yangtze River. The system generates tens of thousands of ship wastewater pollutant data every day. The analysis and visualization of wastewater pollutant discharge data can help the monitoring department to grasp the discharge situation and improve the accuracy of supervision. This system extracts the data to the data warehouse in real-time based on the data extraction technology, improves the query efficiency based on the three-level storage architecture, makes real-time pollutant data statistics based on the stream computing technology, and realizes the visual display of the data based on the front-end and back end-separation technology. The system has been applied in the maritime supervision department to provide a data basis for vessel wastewater pollutant supervision and decision-making. © Published under licence by IOP Publishing Ltd.

关键词： data warehouses

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Scalable Nonlinear Mappings for Classifying Large Sparse data 17th

Scalable Nonlinear Mappings for Classifying Large Sparse Dat...

引用

17th international conference on Advanced data Mining Applications (ADMA)

作者： Li, Xiang Li, Xiao Acad Mil Med Sci Beijing 100091 Peoples R China Adv Inst Big Data AIBD Beijing 100195 Peoples R China Natl Univ Def Technol Changsha 410073 Peoples R China

ISBN: (纸本)9783030954086;9783030954079

Classifying very large sparse data is important in many real-world applications, such as to predict users' gender and profitability based on product ratings. Traditionally, such datasets are often classified with linear models (logistic regression and linear SVM) for scalability, but the predictive accuracy may suffer. Existing applications demonstrate that large sparse datasets often have a low-rank structure. By computing a polynomial approximation to the low-rank data space, a previous work [9] has developed a kernelized classifier, KARMA, to improve the performance on sparse data. However, such method does not scale well to large datasets. In this paper, we develop scalable feature mappings to efficiently approximate the kernels used in KARMA. In experiments, our method inherits the good predictive performance of KARMA on small to medium sized data while showing a significantly better scalability on large datasets.

关键词： Sparse data Classification Feature mapping Scalability

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Research on PM2.5 Pollution Prediction Method in Hefei City Based on CNN-LSTM Hybrid Model 4

Research on PM2.5 Pollution Prediction Method in Hefei City ...

引用

2022 4th international conference on Artificial intelligence Technologies and Applications, ICAITA 2022

作者： Wang, Qiang Zhang, Yiwen School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China School of Computing and Information Technologies National University Manila Philippines

The air quality data has the characteristics of large scale and high time sequence. In the deep learning model, CNN has good feature extraction and expression ability through convolution operation. LSTM has strong time series data processing ability, which effectively solves the long-term dependence problem in the time series data that CNN cannot process. However, a single feature extraction or time series processing method will affect the accuracy of the model. This paper proposes a PM2.5 pollution prediction method based on the CNN-LSTM hybrid model, which can fuse the time series and nonlinear features of the data. By selecting the air quality and meteorological data of Hefei City in Anhui Province from 2018 to 2021 for prediction research, the experimental results show that the CNN-LSTM hybrid model is better than the LSTM model in terms of RMSE, MAE, and R2 evaluation indexes, which reflects higher prediction accuracy. It can not only be used for real-time prediction of PM2.5 pollution, but also for the prediction of other pollutants in the atmosphere. It also provides a reference for the application of CNN-LSTM model in solving other time series big data. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Confidentially computing DNA Matching Against Malicious Adversaries 1

引用

7th international conference on Smart computing and Communication, SmartCom 2022

作者： Tu, Xiaofen Liu, Xin Hu, Xiangyu Li, Baoshan Xiong, Neal N. School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014010 China School of Computer Science Shanxi Normal University Xi’an710062 China National Engineering Research Center for E-Learning Central China Normal University Wuhan430079 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

DNA is one of the most important information in every living thing. The DNA matching experiment is helpful for the study of paternity testing, species identification, gene mutation, suspect determination, and so on. How to study the DNA matching in the case of privacy protection has become the inevitable problems in the research of information security. The Hamming distance can reflect the similarity degree of two DNA sequences. The smaller the Hamming distance is, the more similar the two DNA sequences are. In this paper, the DNA sequence with l length is encoded with a 0–1 string with 3 l length, and the protocol of confidentially computing Hamming distance is designed, which calculated the matching degree of two DNA under the premise of protecting DNA privacy. In addition, in view of the criminal suspect DNA matching problem, we design a secure computation protocol against malicious adversaries using the zero-knowledge proof and the cut-choose method to prevent or find malicious behaviors, which can resist malicious attacks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DNA

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Research on Food Safety data Sharing and Exchange Mechanism 22

Research on Food Safety Data Sharing and Exchange Mechanism

引用

3rd Asia Service Sciences and Software Engineering conference, ASSE 2022

作者： Yu, Jie Li, Danning Chen, Kai Huang, Wei Qin, Meiyuan Qin, Xianjin Guizhou Academy of Testing and Analysis China Guizhou Academy of Sciences Big Data Co.LTD China

ISBN: (纸本)9781450387453

With the development of the economy and the improvement of people's quality of life, the public demand for food taste has gradually changed to the demand for food safety. In order to better facilitate the government to strengthen food safety supervision and protect people's food safety, it is necessary for the government to realize information interaction with enterprises related to the food supply chain and ensure the traceability of food flowing into the market by exchanging and sharing the data of food safety information. With the rapid promotion and popularization of various mobile terminals in the Internet era, the data analysis technology based on artificial intelligence technology is more accurate, which makes the value contained in the data more and more important to people. At present, many fields need the opening and sharing of big data, but there is no reliable data sharing environment in the field of food supervision, and it is still difficult to ensure the traceability of data related to food safety. Blockchain has unique advantages of decentralization and distribution, which can help break the current obstacles of big data sharing and exchange and achieve a high degree of data sharing, interconnection and exchange. Based on the blockchain technology, this paper studies the food safety data sharing and exchange mechanism, combines the blockchain with the distributed file system, constructs the data connection model, stores the shared information on the blockchain, then introduces IPFs and zigzag coding, designs the corresponding control method, and establishes a reliable data sharing and exchange mechanism. The analysis shows that the data sharing and exchange mechanism proposed in this paper can meet the needs of food safety data sharing and exchange. © 2022 Association for computing Machinery.

关键词： big data

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Optimization Strategy for Remote Differential Upgrade of IoT Firmware 11

Optimization Strategy for Remote Differential Upgrade of IoT...

引用

11th Joint international Information Technology and Artificial intelligence conference, ITAIC 2023

作者： Ding, Wenkang Sun, Ruirui Meng, Qinglong Wan, Zhong Cheng, Guanghe Hao, Huijuan Bai, Jinqiang Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350333664

With the continuous increase of IoT devices, the management and optimization of devices become particularly important. The update cycle of device functions is short, and in order to facilitate management and optimize device performance, a fast and convenient upgrade method is needed to solve this problem. Remote upgrade technology is mainly divided into full package upgrade and differential upgrade. Compared to full package upgrade, differential upgrade has obvious advantages in remote upgrade of large files. The main problem with current differential upgrade is the problem of slow generation speed while ensuring a certain size range of differential packages. This article proposes an improved Bsdiff algorithm to solve the problem of slow generation speed of differential packages. Experimental verification shows that the efficiency of remote upgrade has been improved, Improved the speed of differential packet generation and upgrade efficiency while ensuring the size of differential packets. © 2023 IEEE.

关键词： Block Optimization Strategy Bsdiff Difference Algorithm IoT Firmware Libdivsufsort Remote Upgrade

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GroupDiff: Exploring A Unified Graph Structure and High-order Interactions for Group Recommendation 8

GroupDiff: Exploring A Unified Graph Structure and High-orde...

引用

8th international conference on big data computing and Communications (bigCom)

作者： Bai, Jiameng Hu, Bo Zhu, Mengxiao Mao, Zhendong Liu, Anan Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Anhui Peoples R China Univ Sci & Technol China Sch Human & Social Sci Hefei 230027 Anhui Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9781665473842

With the development of social networks, it is becoming popular and convenient for people to join groups and take activities together. How to recommend to a group of users is a challenging problem, since some groups might be ad-hoc with sparse historical interactions, which results in the cold-start problem. Besides, group members usually have different preferences, making it difficult to reach a consensus. Although prior works have proposed to address such issues, they only take the first-order interactions among groups, users and items into consideration, while the high-order interactions are neglected, which are actually very informative for representation learning of group preferences. Furthermore, the interactions among groups, users and items are complex, making it difficult to model and learn from their high-order relationships. To address these issues, in this paper, we design a unified graph structure with heterogeneous nodes. It integrates all three types of interactions, i.e. user-item, group-user and group-item interactions, in one framework, which can facilitate the information diffusion and utilization along with the high-order interactions, and thus be beneficial to group recommendation mechanism design. Besides, based on the unified graph, we also propose GroupDiff, which is a graph convolution network-based model and explores the high-order relationships for representation learning of groups, users and items. Experiment results show the effectiveness of our proposed model.

关键词： group recommendation graph convolutional neural network

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Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang Transform and Feature Fusion Dimensionality Reduction 5

Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang T...

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5th international conference on Artificial intelligence and Pattern Recognition, AIPR 2022

作者： Fan, Lin Yao, Ruiling Wang, Zhongmin School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi 'An Key Laboratory of Big Data and Intelligent Computing China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the development of artificial intelligence, pulse diagnosis has been standardized and objectified. However, there is a lack of research on the extraction and dimensionality reduction of hypertensive pulse features. We propose two effective features for distinguishing pulses of disease samples and a fusion dimensionality reduction method that combines linear and nonlinear dimensionality reduction. The results show that the proposed features and dimensionality reduction method make the classification accuracy of hypertension pulse feature reach 94.23%, and the training time of the classifier is reduced by 47 seconds, which improves the performance in terms of both accuracy and time. © 2022 ACM.

关键词： Hilbert-Huang transform

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Compass: A Penalty-based Routing with High Success Ratio in Payment Channel Networks 8

Compass: A Penalty-based Routing with High Success Ratio in ...

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8th international conference on big data computing and Communications (bigCom)

作者： Li, Donglai Wang, Lei Zhang, Xinwei Fang, Jian Chen, Yuanfang Dalian Univ Technol Sch Software Technol Dalian Peoples R China Shanghai Jiao Tong Univ Ningbo Artificial Intelligence Inst Shanghai Peoples R China

ISBN: (纸本)9781665473842

In the past decade, blockchain-based cryptocurrencies have shown great promise without trusted third parties. However, the lack of scalability prevents them from a wide range of use. Payment Channel Networks (PCNs) such as the Lightning Network are proposed as a promising solution. But unlike traditional networks, transactions on PCNs will fail under insufficient balance in any node of the transaction path. There are some existing multi-path routing schemes to improve the success ratio, while it still remains a challenge to discover the most appropriate paths for each transaction with minimal impact on its subsequent transactions. In this paper, we propose Compass, a novel multi-path routing scheme, to address this challenge. First, we introduce the concept of penalty points. The penalty points are increased for the channel that causes the transaction to fail, and decreased upon a successful transaction for each channel of the path. We use penalty points to choose more reliable paths. After the possible transmission paths are obtained, the transaction amount is split into several sub-transactions which are sent to different paths. The splitting process aims at minimizing the loss of success probability for all channels on the paths, thus making the channels more durable. Compared with state-of-the-art schemes, simulations show that Compass has a higher transaction success ratio, while maintaining a lower latency and transaction fees.

关键词： blockchain payment channel networks multipath routing probability

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