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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1364 条记录，以下是801-810 订阅

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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 ...

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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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Optimization Strategy for Remote Differential Upgrade of IoT Firmware 11

Optimization Strategy for Remote Differential Upgrade of IoT...

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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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Research on Food Safety data Sharing and Exchange Mechanism 22

Research on Food Safety Data Sharing and Exchange Mechanism

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

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

引用

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 ...

引用

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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Accelerating Multi-Object Tracking in Edge computing Environment with Time-Spatial Optimization 9

Accelerating Multi-Object Tracking in Edge Computing Environ...

引用

9th international conference on Advanced Cloud and big data, CBD 2021

作者： Liu, Mengyang Tang, Anran Wang, Huitian Shen, Lin Chang, Yunhan Cai, Guangxing Yin, Daheng Dong, Fang Zhao, Wei School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Institute for Artificial Intelligence Comprehensive National Science Center Hefei China School of Computer Science and Technology Anhui University of Technology Ma'anshan China

ISBN: (纸本)9781665407458

Multi-Object Tracking (MOT) task is a key issue in computer vision. It has capacious application prospects in survelliance, self-driving, and augmented reality. Benefit from the continuous progress of Deep Neural Network (DNN), the accuracy of the MOT algorithm has been significantly improved. Nevertheless, limited to computing power, achieving real-time DNN-based MOT is difficult in embedded systems. In reality, there are many wasteful and unnecessary computations in traditional frame-by-frame full-size video analysis. Therefore, in this paper, we propose a strategy that optimizing the execution of a traditional MOT pipeline in the dimension of time and space. In the temporal dimension, DNN only works in periodic keyframes while using a lightweight model for quickly generating results in the common frames. In the spatial dimension, we design an image density region discriminator to narrow down the input size of DNN. An edge device is introduced to perform end-edge collaborative computing to further accelerating the execution. Additionally, an end-edge parallel computing mechanism is designed that performing dynamic decisions based on the computing power and network environment between end and edge. Moreover, we rebuild the DNN model by TensorRT to optimize the model structure of DNN. By integrating the above approaches, the system can achieve 17.6 ~ 38.1 × speedup ratio, while with 3%~10.4% absolute tracking accuracy sacrifice and can be deployed in an unstable network environment. © 2022 IEEE.

关键词： Deep neural networks

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The Impact of big Five Personality Traits on Augmented Reality Acceptance Behavior: An Investigation in the Tourism Field 13

The Impact of Big Five Personality Traits on Augmented Reali...

引用

13th international conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Labti, Oumayma Bourhim, El Mostafa Laboratory of Research in Management Information and Governance Faculty of Juridical Economic and Social Sciences Ain-Sebaa Hassan II University of Casablanca Route des Chaux et Ciments Beausite BP 2634 Casablanca Morocco EMISYS: Energetic Mechanic and Industrial Systems Engineering 3S Research Center Industrial Engineering Department Mohammadia School of Engineers Mohammed v University Rabat Morocco Moroccan Assoc. of Innov. and Scientific Research in Artificial Intelligence and Extended Reality BP.154 Settat Morocco

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Along with the rapid development of the Internet and mobile devices, the integration of augmented reality (AR) in the tourism sector has become very popular. Utilizing the big five model (BFM) as the theoretical framework, the study examines the role of personality in influencing the behavioral intention (BI) to use mobile augmented reality in the tourism sector (MART). The study further investigates the role of personal innovativeness (PIV) in determining tourists' behavioral intentions to use MART. Quantitative research was carried out to test the conceptual model. This paper strengthened the analysis by implementing PLS-SEM method using data collected from 374 participants. The study results demonstrated that openness to experience (OPN) is a strong predictor of MART use. In addition, agreeableness (AGR), conscientiousness (CONs), extraversion (EX), neuroticism (NR), and personal innovativeness (PIV) have all significant and positive impacts on behavioral intention (BI) to use MART. © 2022 IEEE.

关键词： Augmented reality

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Safe and Effective Elliptic Curve Cryptography Algorithm against Power Analysis 2

Safe and Effective Elliptic Curve Cryptography Algorithm aga...

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2nd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Yao, Jianbo Yang, Chaoqiong Zhang, Tao Hezhou University School of Artificial Intelligence Guangxi Hezhou542899 China Zunyi Normal College Audit Division Guizhou Zunyi563006 China Tianfu Big Data Research Institute Sichuan Chengdu610015 China

ISBN: (纸本)9781665442763

Having high safety and effective computational property, the elliptic curve cryptosystem is very suitable for embedded mobile environment with resource constraints. Power attack is a powerful cipher attack method, it uses leaking information of cipher-chip in its operation process to attack chip cryptographic algorithms. In view of the situation that the power attack on the elliptic curve cryptosystem mainly concentrates on scalar multiplication operation an improved algorithm FWNAF based on RWNAF is proposed. This algorithm utilizes the fragments window technology further improves the utilization ratio of the storage resource and reduces the 'jitter phenomenon' in system computing performance caused by the sharp change in system resources. © 2022 IEEE.

关键词： Public key cryptography

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Graph database-modelled Public Transportation data for Geographic Insight Web Application 24

Graph Database-modelled Public Transportation Data for Geogr...

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24th IEEE/ACIS international Winter conference on Software Engineering, Artificial intelligence, Networking and Parallel/Distributed computing, SNPD 2022

作者： Guillermo, Marielet Rivera, Maverick Concepcion, Ronnie Billones, Robert Kerwin Bandala, Argel Sybingco, Edwin Fillone, Alexis Dadios, Elmer De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Manufacturing Engineering and Management Manila Philippines De la Salle University Department of Transportation and Civil Engineering Manila Philippines

ISBN: (纸本)9798350310412

Public transportation is the key economic driver of a country. The true measure of a country's progress level is scaled on the number of people using the public transportation rather than of people riding private cars. In the Philippines, Western Visayas region (Region VI) is one of the regions which needs extensive support in public transport data organization. Due to the complexity of a public transport network, handling of big data becomes a bottleneck for transport planners. Addressing this problem will help them move forward to more important tasks such as improving transport service for passengers. In this study, a framework was designed in modeling public transportation data. TigerGraph database was utilized to preconnect data and to allow acquisition of geospatial intelligence on route while Django-python was used as the web framework for the geographic insight web application. With the framework and software solution developed, the study intended to make data organization scalable, visualize data relationships, and preconnect data. Preconnecting data in public transport such as terminals, PUV stops, and facilities in conjunction with massive parallel processing (MPP) function, speeds up data analysis. This also enables expanded capability of a system to return answers to queries which need deeper analysis. © 2022 IEEE.

关键词： Python

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