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

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Research on Intelligent Community 'Housekeeper' System Based on Artificial intelligence 5

Research on Intelligent Community 'Housekeeper' System Based...

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5th IEEE international conference on Electronics Technology, ICET 2022

作者： Zeng, Yuting Wu, Qinglin Wuhan Business University School of Information Engineering Wuhan China

ISBN: (纸本)9781665485081

'intelligence' is a new trend of the development of science and technology in the 21st century. With the progress of the society and the acceleration of the pace of life as well as the continuous improvement of people's requirements for themselves, the traditional community has been unable to meet the needs and dependence of modern residents. In the current situation and background of the intelligent community to carry out a 'keeper' system design and system implementation and operation. By using the advantages and technologies of big data and combining the advanced information technology means such as Internet of Things and cloud computing, it provides a good platform environment for the development of smart community, maximizes its functions, improves work efficiency, reduces the waste of manpower and material resources, and becomes an intelligent equipment developed by city and community property managers for more convenient work. © 2022 IEEE.

关键词： Systems analysis

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Multi-user Searchable Attribute Based Encryption for Outsourced big data

Multi-user Searchable Attribute Based Encryption for Outsour...

引用

2022 international conference on Computational Science and Computational intelligence, CSCI 2022

作者： Astudillo, Kyle Ebrahimi, Mahdi Kaplan, Adam CSU Northridge Computer Science Department Northridge United States

ISBN: (纸本)9798350320282

Petabytes of data are generated every minute, and this creates a corresponding demand for servers which ingest, process, store, and maintain this data. The scale of data has become sufficiently large that normal file access and processing present different challenges than in prior decades. In the Web 2.0 era, data owners commonly stored and shared data using on-premise servers. In the past decade, cloud computing has quickly become the dominant hosting paradigm, as it offers data owners the benefits of cost-savings, flexible scaling, and ease of administration. However, cloud computing presents a myriad of security challenges. Attribute based encryption has been key to tackling these challenges. We present a method to allow users to maintain their own secure data on a shared system, while eliminating the time penalty and single point-of-failure associated with traditional authorization techniques. This is implemented as a Representational State Transfer API, removing the burden of managing auxiliary files or encryption libraries when uploading documents. This system also supports data encrypted at rest, removing concerns of administrator access to unencrypted contents in storage. We leverage key policy attribute-based encryption (KP-ABE) to store this data, search its contents, and receive/decrypt this data. We further demonstrate that KP-ABE can be implemented as part of a distributed or centralized authorization method. We compare KP-ABE against AES cryptography, and present system metrics from its execution on representative data. © 2022 IEEE.

关键词： Cryptography

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Defect detection of printed matter based on LSTM progressive Residual network

Defect detection of printed matter based on LSTM progressive...

引用

2022 international conference on Cloud computing, big data Applications and Software Engineering, CBASE 2022

作者： Du, Weidong Lin, Long Xin, Zihao Wang, Hongyuan Southeast University College of Mechanical Engineering Changzhou213164 China Changzhou University School of Computer Science and Artificial Intelligence Changzhou213164 China

ISBN: (纸本)9798350396393

In recent years, as deep learning models represented by CNN have been successfully applied in many computer vision fields, many defect detection methods based on deep learning have also been widely applied in various industrial scenarios. To solve the problem of poor detection effect of printed matter defect detection task based on deep learning, a progressive residual network based on LSTM was proposed. Firstly, the network is divided into multiple stages, and each stage passes through the LSTM cycle network before feature extraction. The input of each stage is the output result of the previous stage and the original input for channel connection. After a layer of convolution dimension enhancement, the feature extractor is entered, the feature graph is extracted, and the convolution dimension reduction is finally carried out as the result of phased restoration. Then, the deep hyperparametric convolutional layer is used to replace the traditional convolutional layer to improve the model performance and accelerate the training process of the network. Experimental results show that the proposed method has better detection effect compared with PRN network, and the improvement of 651%, 326%, and 6.02% on printed samples respectively verifies the effectiveness of the proposed method. © 2022 IEEE.

关键词： Convolution

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Proceedings of the 2022 5th Artificial intelligence and Cloud computing conference, AICCC 2022

Proceedings of the 2022 5th Artificial Intelligence and Clou...

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5th Artificial intelligence and Cloud computing conference, AICCC 2022

ISBN: (纸本)9781450398749

The proceedings contain 41 papers. The topics discussed include: the case for fully Bayesian optimization in small-sample trials;developing a hybrid neural network for part-of-speech tagging and named entity recognition;understanding SyncMap’s dynamics and its self-organization properties: a space-time analysis;microservices containerization in SBCs (single board computers): a cloud edge computing approach;food production forecasting with time series and ensemble modeling methods;hybrid of simplified small world and group counseling optimization algorithms with matured random initialization and variable insertion neighborhood search technique to solve resource constrained project scheduling problems with discounted cash flows;research on the big data collection mode of consumers for innovative products and brand value factors;and the search for areas with high solar energy based on clustering analysis.

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Trends in Mask Recognition Technology Based on Artificial intelligence 7

Trends in Mask Recognition Technology Based on Artificial In...

引用

7th IEEE/ACIS international conference on big data, Cloud computing, and data Science, BCD 2022

作者： Yu, Ga Won Choi, Eun Sung Kang, Young Jin Jeon, Young Joon Jeong, Seok Chan Dong-Eui University Convergence of IT Devices Institute Busan Busan Korea Republic of Dong-Eui University AI Grand ICT Research Center Busan Korea Republic of Dong-Eui University College of Commerce and Economics Dept of E-Business Busan Korea Republic of

ISBN: (数字)9781665465823

ISBN: (纸本)9781665465823

The outbreak of the epidemic at the beginning of the year 2020 to now quickly swept the world, due to the strengthening of control measures. Now everyone wears a mask when going out. In Korea, the number of confirmed cases of corona is still increasing, and the wearing of a mask is mandatory to prevent infection. Therefore, when entering public places, it is necessary to determine whether or not to wear a mask. To achieve better recognition, this paper examines the mask recognition technology using MobileNetV2, Faster R-CNN, SSD-Mask Algorithm, YOLOv5, and Improved YOLOv3 algorithms. Although these mask recognition algorithms show excellent performance, it should be checked whether they work well in various exceptional situations, such as covering the mouth with a hand or a scarf. In the future, we plan to study the mask recognition algorithm that reflects the exceptional situation. © 2022 IEEE.

关键词： Object recognition

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Research on data Analysis of English Translation Trends Based on Artificial intelligence Technology 1

引用

5th international conference on Innovative computing, IC 2022

作者： Liu, Dandan Foreign Languages Department Yunnan Agricultural University Yunnan Kunming650201 China

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

With the rapid development of mankind and economic society in the world today, global cooperation among countries in the world has continued to deepen, the demand for English translation in human society has increased rapidly, and the advancement of artificial intelligence technology has also been advancing. The quality of English translation has been proposed new requirements. This article aims to study the trend analysis of English translation of artificial intelligence big data. Based on the analysis of the advantages of machine translation and the development trend of translation technology, in order to understand the application of artificial intelligence technology to students, this article uses a questionnaire survey investigate the students in three classes of English majors in a college and make relevant suggestions by analyzing the data obtained. The survey results show that machine translation based on artificial intelligence technology is more favored by students, but the problem of mechanization of machine translation also restricts students’ application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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An Optimizing E-govenment Model for data Sharing via Distributed Ledgers

An Optimizing E-govenment Model for Data Sharing via Distrib...

引用

2022 international conference on Artificial intelligence, Information Processing and Cloud computing, AIIPCC 2022

作者： Peng, Sheng Zhu, Linkai Lu, Jiadong Tian, Renhui Song, Shiyang Wang, Wennan Academy of Management Guangdong University of Science and Technology Zhuhai Yingying Technology Co. Ltd. Dongguan China Information Technology School Hebei University of Economics and Business Shijiazhuang050061 China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China Institute of Data Science City University of Macau China Academy of Management Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781665462877

With the development of terminal technology and the expansion of application areas, the value of e-government applications and service requirements continue to increase. To develop and apply e-government, data sharing efficiency is essential. Using the e-government data sharing as a research object, a blockchain-based method is developed for optimizing data transfer. Blockchain technology is used for storing and delivering distributed data. Rather than distributing data nodes randomly, our network scenario model employs a matrix lattice model based on a complete undirected graph. Dual optimization is then applied to solve the problem. In order to find the optimal solution, a mathematical model and an artificial neural network are developed. To verify the performance and packet loss rate of the government data transmission method, a case study is conducted. data Sharing is optimized to increase the lifetime of static aggregation nodes by at least 30% and lifetime of static aggregation nodes by at least 20%. The network lifetime can be extended by optimizing static aggregation nodes. Furthermore,it effectively improves the robustness. © 2022 IEEE.

关键词： Neural networks

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引用

5th Artificial intelligence and Cloud computing conference, AICCC 2022

作者： Elouali, Aya Mora, Higinio Gimeno, Francisco J. Mora Department of Computer Science Technology and Computation University of Alicante Alicante Spain

ISBN: (纸本)9781450398749

Edge-cloud collaborative processing for IoT data is a relatively new approach that tries to solve processing and network issues in IoT systems. It consists of splitting the processing done by a Neural Network model into edge part and cloud part in order to solve network, privacy and load issues. However, it also has it shortcomings such as the big size of the edge part's output that has to be transmitted to the cloud. In this paper, we are proposing a data transmission reduction method for edge-cloud collaborative solutions that is based on data similarities in stationary objects. The performed experiments proved that we were able to reduce 62% of the data sent. © 2022 ACM.

关键词： data transfer

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Performance Analysis of Matrix Multiplication Based on Serverless Platform 1

引用

2nd international conference on Artificial intelligence, Robotics, and Communication, ICAIRC 2022

作者： Chen, Junzhou Lu, Jiren Ma, Hanpei Westa College Southwest University Chongqing400715 China School of Statistics and Information Shanghai University of International Business and Economics Shanghai201620 China School of Information and Computer Taiyuan University of Technology Taiyuan030000 China

ISBN: (数字)9789819945542

ISBN: (纸本)9789819945535

Over the past few years, serverless computing has become increasingly important as a new field that dramatically reduces costs, reduces latency, improves scalability, and eliminates server-side management. big data is also increasingly used in various areas, such as finance, transportation, and telecommunications. Large amounts of data information must be stored in high-dimensional matrices for large-scale scientific computations. This paper combined serverless computing with high-dimensional matrices by using the MapReduce model to implement a serverless architecture on Aliyun to multiply high-dimensional matrices. This paper used observation, comparison, and control variable methods to conduct experiments and concluded by analyzing a large amount of experimental data. This paper found that the change in the number of workers will affect the total working time, the ratio among total working time, calculation time and communication time. The matrix size change will affect the computation time ratio to total working time and communication time to total working time. Moreover, this paper infers an optimal number of workers for each size matrix, which minimizes the total execution time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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An allocation method of crowdsourcing tasks for protecting fairness of participants 23

An allocation method of crowdsourcing tasks for protecting f...

引用

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： Xiao, Lei Gao, Zhigang Xu, Ruichao Wu, Bo Zheng, Leilei Zhao, Wei Cen, Weipeng He, Xuanzhang College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou China School of Computer Science Tokyo University of Technology Hachioji Japan Interdisciplinary Graduate School University of Yamanashi Kofu400-8511 Japan

ISBN: (纸本)9781665494571

With the increasingly powerful functions of mobile devices and the popularity of wireless networks, spatial crowdsourcing has become a new service model. This paper studies the task assignment problem in spatial crowdsourcing, which reduces the commuting cost of participants while ensuring the fairness of participants under the condition of task execution order constraints. This paper first proposes the Fairness Monte Carlo Tree Search (FMCTS) task allocation algorithm, ensuring that the maximum attendance difference of participants is below the set value while reducing the participants' commuting costs. After that, to optimize the efficiency of the algorithm, a FMCTS-Guided (FMCTSG) task allocation algorithm is proposed, which can reduce the running time of the algorithm. Finally, for non-emergency tasks, a Delayed FMCTS (DFMCTS) algorithm is proposed, which can further reduce the commuting cost of task participants. The experimental results show that the task allocation method proposed in this paper can effectively reduce the commuting cost of participants while ensuring the fairness of participants. © 2021 IEEE.

关键词： Crowdsourcing Costs Monte Carlo methods Smart cities Wireless networks Mobile handsets Resource management

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