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

共 1353 条记录，以下是61-70 订阅

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Parallel solution and optimization of microphysics module in GRAPES physics process 2

Parallel solution and optimization of microphysics module in...

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2nd international conference on big data, Computational intelligence, and Applications, BDCIA 2024

作者： Xu, Aokang Jia, Jinfang School of Computer Technology and Applications Qinghai University Qinghai Xining810016 China Intelligent Computing and Application Laboratory of Qinghai Province Qinghai Xining810016 China

ISBN: (纸本)9781510689053

Global and Regional Assimilation and Prediction System (GRAPES) is a domestically developed numerical weather prediction system in China. The microphysical process is the key physical process of cloud formation and precipitation and is also one of the hot spots in the calculation of the whole model. Despite the parallel processing capabilities of MPI and OpenMP, the computation time of the microphysics process remains relatively long. In order to achieve finer-grained parallelization, the code is ported to Graphics Processing Unit (GPU) using a combination of memory coalescing and array scalarization methods. In addition, to address the issue of underutilization of GPU resources when multiple processes share a single GPU, the Compute Unified Device Architecture (CUDA) Multi-Processing Service (MPS) is introduced in the microphysics module to improve GPU utilization. Experimental results show that for a resolution of 50km, excluding data transfer time, the speedup achieved by using 4 GPUs in conjunction with 36 CPU cores reaches 11.59, and the speedup excluding data transfer time is 18.59. It has been verified that the parallel computation of the microphysics process on GPUs is stable and correct. © 2025 SPIE.

关键词： Graphics processing unit

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Financial data Analysis Methods Research Based on Artificial intelligence 12th

Financial Data Analysis Methods Research Based on Artificial...

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12th international conference on Frontier computing, FC 2022

作者： Xu, Weishuang School of Accountancy Shandong Youth University of Political Science Shandong Jinan250103 China

ISBN: (纸本)9789819914272

In the era of big data, with the reduction of unit value density of data, it is more meaningful to fully mine the value contained in data resources than to expand the scale of data. Artificial intelligence based on data, algorithms and computing power has more advantages in data value mining than traditional data analysis method. Based on this, this paper takes financial data as the research object. On the basis of analyzing the urgency of financial data value mining, the characteristics of financial data and analyzing the limitations of traditional financial data analysis method, a financial data analysis framework based on artificial intelligence is constructed. In the constructed analysis framework, big data platform is the center of data collection and store. computing power and algorithm based on artificial intelligence is the main analysis tools. data analysis results present in different forms is the basis for decision making. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data handling

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Semi-supervised network regression with Gaussian process

Semi-supervised network regression with Gaussian process

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Kim, Myungjun Lee, Dong-gi Shin, Hyunjung Ajou Univ Dept Artificial Intelligence Suwon South Korea Ajou Univ Dept Psychiat Sch Med Suwon South Korea Ajou Univ Dept Ind Engn Dept Artificial Intelligence Suwon South Korea

ISBN: (纸本)9781665421973

In recent years, there has been a rapid growth in interest of using network-based machine learning. They offer the capacity to handle data that exist on irregular and complex structure with interactions between data points. In this paper, we present a semi-supervised regression model utilizing network-based Gaussian process. The proposed method constructs a Gaussian process prior using information from a given network. However, it incurs high computational costs from the required inversions to produce the predictive output and model selection. To overcome the difficulty, we further propose an approximated version that avoids matrix inversion. The proposed method was applied to several regression problems to validate the empirical performance and effectiveness in situations with limited amount of labeled data.

关键词： Graph-based semi-supervised regression Network-based learning Network-based Gaussian process

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Curriculum System Construction of Application Major in Higher Vocational Colleges Based on big data Technology 12th

Curriculum System Construction of Application Major in Highe...

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12th international conference on Frontier computing, FC 2022

作者： Xiao, Yeqing Sichuan Top IT Vocational Institute Chengdu611743 China

ISBN: (纸本)9789819914272

The society has a huge demand for big data, artificial intelligence and cloud computing jobs, so it is urgent to cultivate a new generation of information technology talents. On this basis, this paper discusses the student training curriculum system focusing on computer and data application, introduces the "berufsbank f ü R berufszentrum project praxis" training curriculum system, and innovates and transforms the training curriculum system into big data. This requires constantly improving the quality of talent training. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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data Modeling Algorithm of Traditional Aesthetics and Modern Art Design Under the Background of big data 1

Data Modeling Algorithm of Traditional Aesthetics and Modern...

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1st international conference on Applied Artificial intelligence and computing, ICAAIC 2022

作者： Han, Aiju Sichuan University of Media and Communications College of Communication and Management Chengdu610000 China

ISBN: (纸本)9781665497107

Under the background of big data, traditional aesthetic design thinking is an important professional basic course in current art design teaching. Modern art design is a thinking training for cultivating college students to be designers in the future. It is an experience of laws and national folk culture, and it is the future design benchmark that leads contemporary innovative life, aesthetic taste and material spirit. Starting from the connotation and practical significance of traditional aesthetics, this paper analyzes the influence of traditional aesthetics on modern art design, and discusses data modeling algorithms. © 2022 IEEE.

关键词： big data

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Decentralized data Center Analysis of Economic and Financial Development in international Trade in the Era of big data 1

Decentralized Data Center Analysis of Economic and Financial...

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1st international conference on Applied Artificial intelligence and computing, ICAAIC 2022

作者： Chen, Lei Shandong Management University Jinan250000 China

ISBN: (纸本)9781665497107

As the mainstream development trend of world trade, big data has also brought a new direction to the development of international trade theory. Based on the overview of international trade theory, this paper analyzes the new development of international trade in the era of big data, using blockchain the decentralization of the data center of the company extracts features from the economic and financial data in international trade. The experimental results show that the efficiency of multi-dimensional data analysis in international trade will increase by 7.3%. The virtual electronic currency based on the idea of decentralization and hashing algorithm has flourished and developed rapidly in recent years. Its unique currency issuance and circulation method based on algorithm rather than credit is considered to be a part of the currency development process. revolution. © 2022 IEEE.

关键词： international trade

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Design and Research of Smart Health Management System Based on big data 2

Design and Research of Smart Health Management System Based ...

引用

2022 2nd international conference on Health big data and Smart Sports, HBDSS 2022

作者： Liu, Ping Chongqing Normal University Chongqing China

ISBN: (纸本)9783800760428

Health is the basis of individual life and the premise of social progress, but its data is difficult to collect, process and synchronize in health management. This leads to difficulties in health prediction and timely recognition. In order to address these issues and change the passive position of individuals in health management, the aim of this study is to improve the ability to intervene early on risk factors that may lead to disease and the timeliness of health status understanding. After obtaining the needs of people of all ages in health management through questionnaire survey, the technology of big data, cloud computing, artificial intelligence, intelligent perception and intelligent positioning is deeply integrated, and the design scheme of intelligent health management system with multi-end integration is designed, and the key technologies and processes are elaborated. The system includes health data collection, cloud diagnosis, online consultation, emergency call, integrated registration and health information recommendation and other functions. 32 people of different ages were randomly selected for interviews, and the conclusion was drawn that the platform could solve the pain points of health management and effectively improve the level of health management. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： big data

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The Spread of Artificial intelligence Technology Challenges the Bottom Line of Privacy Protection 7

The Spread of Artificial Intelligence Technology Challenges ...

引用

7th international conference on Cloud computing and big data Analytics, ICCCBDA 2022

作者： Wu, Ying Shanghai University of Political Science and Law Shanghai China

ISBN: (纸本)9781665487115

Artificial intelligence is prone to or has caused devastating damage through technical means to access data. But massive amounts of personal information data underpin AI applications;data is the foundation of AI, and it is the use of big data, increased computing power, and algorithmic breakthroughs that have allowed AI to develop rapidly and be widely used. The complexity of the data environment, algorithms and models behind the development and operation of AI systems, and the deliberate secrecy of governments and private actors are some of the factors that undermine the public's understanding of how AI systems can impact on human rights and society. Can they co-exist and how?. © 2022 IEEE.

关键词： Privacy Virtual assistants Computational modeling Government Companies big data Market research

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Scalable Federated Unlearning via Isolated and Coded Sharding 33

Scalable Federated Unlearning via Isolated and Coded Shardin...

引用

33rd international Joint conference on Artificial intelligence (IJCAI)

作者： Lin, Yijing Gao, Zhipeng Duo, Hongyang Niyato, Dusit Gui, Gui Cui, Shuguang Ren, Jinke Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Chinese Univ Hong Kong Shenzhen Future Network Intelligence Inst Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen Guangdong Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Cent South Univ Sch Automat Changsha Hunan Peoples R China

ISBN: (纸本)9781956792041

Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the federated unlearning process often introduces extensive storage overhead and consumes substantial computational resources, thus hindering its implementation in practice. To address this issue, this paper proposes a scalable federated unlearning framework based on isolated sharding and coded computing. We first divide distributed clients into multiple isolated shards across stages to reduce the number of clients being affected. Then, to reduce the storage overhead of the central server, we develop a coded computing mechanism by compressing the model parameters across different shards. In addition, we provide the theoretical analysis of time efficiency and storage effectiveness for the isolated and coded sharding. Finally, extensive experiments on two typical learning tasks, i.e., classification and generation, demonstrate that our proposed framework can achieve better performance than three state-of-the-art frameworks in terms of accuracy, retraining time, storage overhead, and F1 scores for resisting membership inference attacks.

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Some Classes of Cryptographic Power Functions and Permutation Polynomials with Low (-1)-Differential Uniformity in Cyber Security

Some Classes of Cryptographic Power Functions and Permutatio...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive intelligence and computing, 2023 international conference on Cloud and big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Liu, Qian Dong, Xiaobei Chen, Zhixiong Liu, Ximeng Xu, Li College of Computer and Data Science Fuzhou University Fuzhou China Putian University Fujian Key Laboratory of Financial Information Processing Putian China College of Computer and Data Science Fuzhou University Key Laboratory of Information Security of Network Systems Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China

ISBN: (纸本)9798350304602

Cryptographic functions with low differential uniformity have wide applications in cryptography and cyber security. In this paper, we further investigate c-differential uniformity proposed by Ellingsen et al. (IEEE Transactions on Information Theory. 2020, 66(9): 5781-5789). Specifically, two classes of power functions with low (1)-differential uniformity over finite fields with odd characteristic are obtained. In addition, a class of permutation polynomials and P1N or AP1N functions over F3n are proposed. These functions with low c-differential uniformity are utilized to resist against c-differential attacks in cyberspace from those with the usual low differential uniformity. © 2023 IEEE.

关键词： Cybersecurity

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