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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71-80 订阅

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Research on Decentralized Algorithm of Infrastructure Project Cost Management Aided by big data 1

Research on Decentralized Algorithm of Infrastructure Projec...

引用

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

作者： Shi, Wene Taizhou Institute of Vocational Technology College Zhejiang Taizhou318000 China

ISBN: (纸本)9781665497107

This paper will use the characteristics of decentralized trust management of blockchain to study the decentralized solution of trusted data sharing, and to solve the problem of storage of shared data, propose the block structure of shared data for blockchain, which will help big data A brief overview is given, and a detailed analysis of the impact of big data on project cost management work is carried out. Finally, an in-depth exploration of effective strategies for project cost management under big data is carried out to promote the development of diversification of project cost management work to 7.6%. The infrastructure shared data storage block structure, and the information of the shared data is stored in the blockchain body;in view of the data access request and service problems in data sharing, a blockchain-based, decentralized cost management algorithm is proposed. © 2022 IEEE.

关键词： Blockchain

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A Greedy Algorithm for Generating Malware Signatures Based on Association

A Greedy Algorithm for Generating Malware Signatures Based o...

引用

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

作者： Joe, Woo-Jin Kim, Hyong-Shik Chungnam Natl Univ Dept Comp Sci & Engn Daejeon South Korea Chungnam Natl Univ Dept Artificial Intelligence Daejeon South Korea

ISBN: (纸本)9781665421973

Malware signature should be compact to reduce the possibility of being bypassed. However, previous studies combine unnecessary features because they cannot distinguish associated features. This study proposes an algorithm for generating effective and compact signatures, taking into account the association of features. The proposed algorithm is based on a greedy approach that chooses a feature with the lowest association with the currently combined features. As a result of conducting a family detection experiment on 4,443 samples consisting of 869 families, it was confirmed that the signature size was about 90 times smaller and the performance was 2.8% higher than in the existing method.

关键词： malware family variant signatures API

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Dual Watermarking Based on DCT with Human Visual Characteristics for Authentication and Copyright Protection

Dual Watermarking Based on DCT with Human Visual Characteris...

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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： Ernawan, Ferda Jie, Wong Shu Bakar, Suraya Abu Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Malaysia

ISBN: (纸本)9798350304602

Nowadays, multimedia contents like images are easily distributed globally because of the widespread usage of information and communication technologies. Digital watermarking is an approach to prevent attacks or modification on images that might lead to serious problems like spreading of fake news, image piracy and illegal distribution of images. This research presents dual image watermarking based on human visual characteristics for authentication and copyright protection. The scheme aims to embed a watermark by modifying $U$ coefficients of DCT-SVD with minimal distortion to the human eye. Additionally, the scheme demonstrates embedding authentication bits in the LSB for efficient tamper localization, enabling the detection of modifications made to the original image. The experimental results validate the effectiveness of the proposed scheme, showcasing superior performance in terms of imperceptibility, robustness, and tamper localization compared to existing watermarking schemes subjected to the same attacks. The scheme's ability to embed undetectable watermarks ensures copyright protection and authenticity verification, while its robustness against various image processing attacks. The proposed scheme offers a reliable approach for securing digital image content by ensuring imperceptibility, robustness, and tamper localization. © 2023 IEEE.

关键词： Authentication

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Construction of Power Forecasting and Environmental Protection data Platform Based on Smart Grid big data 1

Construction of Power Forecasting and Environmental Protecti...

引用

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

作者： Li, Guang-Ye Zhang, Jia-Xin Wen, Xin Xu, Lang-Ming Yuan, Ying State Grid Liaoning Electric Power Supply Co. Ltd Shenyang110004 China Liaoyang Branch of State Grid Liaoning Electric Power Supply Co. Ltd Liaoyang111000 China School of Business Administration Northeastern University Shenyang110169 China

ISBN: (纸本)9781665497107

In today's era, the smart grid is the carrier of the new energy technology revolution and a very critical development stage for grid intelligence. In the process of smart grid operation, maintenance and maintenance, many heterogeneous and polymorphic data can be formed, that is to say big data. This paper analyzes the power big data prediction technology for smart grid applications, and proposes practical application strategies In this paper, an in-depth analysis of the relationship between cloud computing and big data key technologies and smart grid is carried out, and an overview of the key technologies of electric power big data is carried out. © 2022 IEEE.

关键词： Smart power grids

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Dynamic Weight Distribution Method of Loss Function Based on Category Theory 20th

Dynamic Weight Distribution Method of Loss Function Based on...

引用

20th international conference on Intelligent computing (ICIC)

作者： Chen, Jiehao Zhao, Heng Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China

ISBN: (纸本)9789819756650;9789819756667

Imbalanced datasets are prevalent in real life, which have a skewed data distribution. In the research of imbalanced datasets, the inherent patterns of minority class affect the generalization performance of Machine Learning models. The re-weighting/re-samplingmethods are used to address the issue of imbalanced datasets. However, the above methods are formulated based on empirical and heuristic rules. A theoretical framework is required in the presentation of the re-weighting/re-sampling methods. Category Theory is used as a framework to analyze the training process of Machine Learning models on imbalanced data. Based on the analysis, a method named Dynamic Weight Adjustment (DWA) is proposed to improve the generalization performance of models on imbalanced data. The imbalanced datasets for binary classification and multi-classification problems are used to verify the effectiveness of the DWA. The results of the DWA are the best in both imbalanced datasets for binary classification and multi-classification problems.

关键词： Imbalanced datasets Loss Function Dynamic Weight Adjustment Category Theory

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Application Research of Cloud computing Technology in Computer data Processing 2

Application Research of Cloud Computing Technology in Comput...

引用

2022 2nd international conference on Artificial intelligence, big data and Algorithms, CAIBDA 2022

作者： Han, Shuang Hainan Technology and Business College Hainan Haikou570203 China

ISBN: (纸本)9783800760268

In the context of the era of big data, it not only creates a lot of opportunities for the development of modern society but also poses more challenges. If the society wants to develop better, it must take good technical means to process computer data to accurately mine the contained value. The application of cloud computing technology can achieve this purpose. Based on this, this paper analyzes the correlation between cloud computing technology and computer data processing through an overview of cloud computing technology, and then analyzes the cloud computing process based, so as to provide support for better application of cloud computing technology. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Cloud computing

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Industrial Cyber Intelligent Control Operating System that Hybrid with IEC 61499 and big data on Edge computing 5

Industrial Cyber Intelligent Control Operating System that H...

引用

5th IEEE international conference on Artificial intelligence and big data, ICAIBD 2022

作者： Su, Weibin MacHica, Ivy Kim Xu, Gang He, Zhengfang Kong, Yanchun University of Southeastern Philippines College of Information and Computing Davao Philippines Yunnan Technology and Business University Edge Computing Network Center Kunming China Faculty of Construction Engineering Kunming Metallurgy College Kunming China

ISBN: (纸本)9781665499132

IEC 61499 and big data systems, which are also distributed architecture, but they belong to two independent and unrelated platforms due to different network and control concepts. With the development of intelligent manufacturing factories, the big data generated by the process of industrial production is facing unprecedented challenges of storage and data processing. The current approach is to put industrial big data on cloud computing and the industrial automation control program on the side that is close to physical equipment. This architecture has the problems of low efficiency, insufficient security and poor real-time performance. The purpose of this paper is to design an architecture for intergration IEC61499 and big data. It can not only reduce the cost of building a smart factory, but also increase the security and data access velocity. More importantly, it can meet the real-time performance of industrial control. © 2022 IEEE.

关键词： Edge computing

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Sym-Fed: Unleashing the Power of Symmetric Encryption in Cross-Silo Federated Learning 22

Sym-Fed: Unleashing the Power of Symmetric Encryption in Cro...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / bigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wang, Jinzhao Tian, Wenlong Li, Ruixuan Tang, Junwei Ye, Xuming Wan, Yaping Xu, Zhiyong Univ South China Sch Comp Sci & Technol Hengyang Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore Singapore Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Suffolk Univ Math & Comp Sci Dept Boston MA 02114 USA

ISBN: (纸本)9798350381993;9798350382006

With the increasing number of big data applications, large amounts of valuable data are distributed in different organizations or regions. Federated Learning (FL) enables collaborative model training without sharing sensitive data and is widely used in AI medical diagnosis, economy, and autonomous driving scenarios. However, it still leaks the privacy from the gradient exchange in federated learning. What's worse, state-of-the-art work, such as Batchcrypt, still suffers from computational overhead due to a considerable amount of computation and communication costs caused by homomorphic encryption. Therefore, we propose a novel symmetric key-based homomorphic encryption scheme, Sym-Fed. To unleash the power of symmetric encryption in federated learning, we combine random masking with symmetric encryption and keep the homomorphic property during the gradient exchange in the federated learning process. Finally, the security analysis and experimental results on real workloads show that our design achieves performance improvement 6x to 668x and reduces the communication overhead 1.2x to 107x compared with the state-of-the-art work, BatchCrypt and FATE, without model accuracy degradation and security compromise.

关键词： Federated Learning Symmetric Encryption Homomorphic Encryption

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Presto Pack: Resource-aware Workload Placement for Higher Performance and Reliability

Presto Pack: Resource-aware Workload Placement for Higher Pe...

引用

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

作者： Cayiroglu, Cem Zhuang, Zhenyun Chattopadhyay, Biswapesh Pandian, Prithvi Li, Chen Facebook Inc 1 Hacker Way Menlo Pk CA 94025 USA

ISBN: (纸本)9781665421973

Presto is one of the most popular OLAP (Online Analytical Processing) query engines. A Presto cluster consists of multiple worker nodes that execute tasks. As of today, Presto's workload placement is not resource-aware, which leads to wildly unbalanced resource usages across workers, jeopardizing business-important metrics such as reliability, performance and efficiency. We aim to add intelligence to Presto workload placement by making it resource-aware. Referred to as Presto Pack, this feature is mainly implemented on Presto coordinator. It takes into account of two resource types: memory and CPU on worker nodes. Evaluation results show that Presto Pack can achieve much more balanced CPU and memory usage across workers, less reliability issues, and higher throughput. Specifically, Presto Pack can improve overall query throughput by 23% on a per-server basis, leading to higher levels of concurrency and reliability.

关键词： Measurement Concurrent computing conferences Memory management Production big data Throughput

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A Review of Knowledge Graph Recommendation Systems Based on VOSviewer 9

A Review of Knowledge Graph Recommendation Systems Based on ...

引用

9th international conference on Cloud computing and big data Analytics, ICCCBDA 2024

作者： Zhang, Deqing Abisado, Mideth College of Computing and Information Technologies National University Manila Philippines Anhui Sanlian University Hefei China

ISBN: (纸本)9798350373554

In the era of artificial intelligence, how recommendation algorithms can accurately and effectively solve the problem of matching individuals' interests and needs in the data ocean, so that they can quickly obtain the required information, has been a hot research topic in recent years. With the help of VOSviewer, this paper makes a visual analysis of the relevant core literature in the past five years, and summarizes the four main research directions currently: (1)Improving the performance of recommendation systems through graph embedding technology.(2)Explored knowledge graph perception propagation methods from the perspective of attention mechanism to improve recommendation quality.(3)The widespread application of technologies such as deep learning in knowledge graph recommendation systems has improved recommendation performance.(4)Innovate multi task frameworks to enhance recommendation effectiveness. Finally, summarize its application classification, corresponding technological development, and academic achievements, and propose future technological development directions for recommendation systems. © 2024 IEEE.

关键词： Recommender systems

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