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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1561 条记录，以下是271-280 订阅

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Enhancing Cloud computing data Security in Attribute-based Encryption Schemes using Cryptographic Algorithm 5

Enhancing Cloud Computing Data Security in Attribute-based E...

引用

5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Yuvarani, R. Mahaveerakannan, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350367171

Attribute-Based Encryption (ABE) is a powerful cryptographic technology used in cloud computing and data security. It allows data owners to control access to encrypted data based on specific characteristics rather than individual user IDs. This makes sure that only systems that satisfy certain requirements or people, who are allowed may decode and view the data. Because of its dynamic and adaptable approach to access management, ABE is a great fit for situations with complex and dynamic access requirements. In response to the threat posed by Quantum computing, this research study focused on developing Post-Quantum ABE schemes that maintain security while also addressing attributes like verifiability, user privacy, and revocability. A novel ABE system founded on rank metric codes has been proposed. The proposed approach is built on low rank parity check codes with a robust resistance against diverse forms of attacks with a compact key size and rapid execution time, demonstrating notable efficiency when compared to other cryptographic systems. This represents a substantial stride forward in enhancing Cloud computing in the era of Quantum computing. © 2024 ieee.

关键词： Cloud security

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Evolving cognitive Models: A Novel Approach to Verbal Learning 6

Evolving Cognitive Models: A Novel Approach to Verbal Learni...

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6th international conference on cognitive Machine Intelligence

作者： Javed, Noman Bennett, Dmitry Bartlett, Laura K. Lane, Peter C. R. Gobet, Fernand London Sch Econ Ctr Philosophy Nat & Social Sci London England Univ Hertfordshire Sch Phys Engn & Comp Sci Hatfield Herts England

ISBN: (纸本)9798350386738;9798350386721

A common goal in cognitive science involves explaining/predicting human performance in experimental settings. This study proposes a single GEMS computational scientific discovery framework that automatically generates multiple models for verbal learning simulations. GEMS achieves this by combining simple and complex cognitive mechanisms with genetic programming. This approach evolves populations of interpretable cognitive agents, with each agent learning by chunking and incorporating long-term memory (LTM) and short-term memory (STM) stores, as well as attention and perceptual mechanisms. The models simulate two different verbal learning tasks: the first investigates the effect of prior knowledge on the learning rate of stimulus-response (S-R) pairs and the second examines how backward recall is affected by the similarity of the stimuli. The models produced by GEMS are compared to both human data and EPAM - a different verbal learning model that utilises hand-crafted task-specific strategies. The models automatically evolved by GEMS produced good fit to the human data in both studies, improving on EPAM's measures of fit by almost a factor of three on some of the pattern recall conditions. These findings offer further support to the mechanisms proposed by chunking theory (Simon, 1974), connect them to the evolutionary approach, and make further inroads towards a Unified Theory of Cognition (Newell, 1990).

关键词： chunking evolution GEMS LTM STM CHREST

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A Novel Multi-index Method of Measurement Optimization in Entrepreneurial Ecosystem with Big data 3

A Novel Multi-index Method of Measurement Optimization in En...

引用

3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Fan, Zhen College Management Guangdong University of Science and Technology Guangdong Dongguan China

ISBN: (纸本)9798331519032

This project takes entrepreneurship ecology as the research object, takes multiple variables as the core, integrates a variety of core elements, provides comprehensive and accurate information for entrepreneurs, investors and policy makers, and provides a scientific basis for intelligent decision-making in complex entrepreneurial situations. Through the analysis of multiple indices, our empirical results show that the method can more truly reflect the entrepreneurial ecological environment. This project intends to use multivariate statistical analysis methods to comprehensively evaluate the current situation and development potential of entrepreneurship ecological environment, reveal the existing problems and problems, and provide decision-making basis for the decision-making and decision-making of the government and enterprises. The research results of this project will lay the foundation for the improvement of the innovation ability and innovation ability of new ventures. © 2024 ieee.

关键词： entrepreneurial ecosystem multi-index approach quantitative optimization

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Research on data lakehouse architecture for grid business data 3

Research on data lakehouse architecture for grid business da...

引用

3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Meng, Yu Li, Qiang Wang, Ziheng State Grid Information and Telecommunication Group Co. Ltd. Beijing100052 China

ISBN: (纸本)9798350352658

With the development of national informatization construction and big data technology, the traditional database design can no longer support the efficient storage and real-time analysis of massive data, and it is difficult to adapt to increasingly complex business scenarios with high reliability and timeliness. Therefore, to build a high-performance and low-cost big data processing system, combined with the characteristics of the grid business data, this paper proposes an optimized big data architecture based on data lakehouse, using the pre-computing and ETL capabilities of the Doris database instead of the original complex technology stacks, and designing a Doris table structure that meets the statistical analysis requirements of the five-level organizational structure of State Grid to support data analysis result query. The lightweight architecture supports high concurrency, sub-second latency, and multi-dimensional analysis. Finally, a big data analysis system for power grid business operation is realized with less development cost and lower deployment difficulty, which can quickly dig deep information of application and user data and improve platform operation capability. © 2024 ieee.

关键词： data lakehouse data processing database

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CryptoCertify: Certificate Validation and Authentication Using Blockchain Technology 1

CryptoCertify: Certificate Validation and Authentication Usi...

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1st international conference on cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Jadhav, Balasaheb Maharnawar, Niranjan Lakhotiya, Rushikesh Malpani, Rushikesh Ligde, Vaidehi Savale, Phalguni Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune411037 India Sagitech Solutions Pvt. Ltd. India

ISBN: (纸本)9798350361780

CryptoCertify is a new method for certificate validation and verification in the dynamic field of digital certification. With the implementation of Solidity on the Polygon blockchain and simple integration with Metamask and IPFS, CryptoCertify aims to improve security and usefulness. A decentralized and resistant solution is required since traditional certificate validation methods have problems with centralization and fraud. By establishing an irreversible and trustless infrastructure, smart contracts implemented with Metamask and written in Solidity significantly reduce the risks related to fake certificates. The certificate information is safely kept on IPFS and the Polygon blockchain, forming a distributed ledger that is cryptographically secure and time-stamped. By using Metamask, stakeholders might independently validate certificates, thereby improving validation process transparency. By integrating blockchain, IPFS, Metamask, and decentralization, this research contributes to the growing body of knowledge on safe digital certification methods. It additionally offers a solution that is versatile, scalable, and resistant to fraud for certificate validation and authentication in the future. © 2024 ieee.

关键词： Smart contract

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Service Function Chaining on SDN/NFV based programmable data plane 15

Service Function Chaining on SDN/NFV based programmable data...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhanapani, Meignanamoorthi Vetriselvi, V. Chennai600025 India

ISBN: (纸本)9798350370249

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are transforming next-generation networks. SDN's data plane programmability unlocks new levels of network flexibility. NFV simplifies management of essential network functions like firewalls and intrusion detection systems. Service Function Chaining (SFC) offers increased manageability by defining ordered sequences of these functions as virtual chains. However, enforcing SFC through traditional network switches is complex. This paper proposes a novel architecture that leverages the power of SDN and NFV for efficient SFC packet processing. By utilizing the programmability of modern switches, our design enforces SFC directly on the data plane. Experimental results demonstrate significant performance improvements in processing time compared to the widely used OpenVSwitch. This approach paves the way for high-performance and scalable SFC enforcement in next-generation networks. © 2024 ieee.

关键词： Intrusion detection

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SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems

SplitVAEs: Decentralized scenario generation from siloed dat...

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2024 ieee international conference on Big data, Bigdata 2024

作者： Islam, H. M. Mohaimanul Vo, Huynh Q. N. Ramanan, Paritosh Oklahoma State University School of Industrial Engineering and Management StillwaterOK74078 United States

ISBN: (纸本)9798350362480

Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to the existence of data silos resulting from computational and logistical bottlenecks. In this paper, we present SplitVAE, a decentralized scenario generation framework that leverages variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of experiments on distributed memory systems, we demonstrate the broad applicability of SplitVAEs in a variety of domain areas that are dominated by a large number of stakeholders. Furthermore, these experiments indicate that SplitVAEs can learn spatial and temporal interdependencies in large-scale networks to generate scenarios that match the joint historical distribution of stakeholder data in a decentralized manner. Lastly, the experiments demonstrate that SplitVAEs deliver robust performance compared to centralized, state-of-the-art benchmark methods while significantly reducing data transmission costs, leading to a scalable, privacy-enhancing alternative to scenario generation. © 2024 ieee.

关键词： decentralized learning scenario generation stochastic optimization variational autoencoders

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Research on Natural Language Generation System of BERT Model Computer Big data Technology 6

Research on Natural Language Generation System of BERT Model...

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6th ieee international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Zhang, Jianqin Jiangsu Union Technical Institute Nanjing Engineering Branch Nanjing China

ISBN: (纸本)9798350374315

This paper focuses on the application and research of pre-trained model BERT in natural language generation system to meet the needs of natural language processing tasks in the era of big data. With the emergence of massive data, it is of great significance to use big data technology to understand and generate natural language efficiently. This study aims to build and optimize a natural language generation system based on BERT model, which can fully tap and utilize large-scale unannotated text data and capture complex context dependencies through deep two-way Transformer architecture to achieve accurate and smooth natural language generation. First, this paper discusses the core mechanism of BERT model and its superiority in natural language understanding, and analyzes its ability to effectively learn massive text resources in big data environment. Then, we design a BERT model extension scheme that ADAPTS to big data environment. By combining distributed computing framework and targeted model optimization strategy, the training efficiency and generalization performance of the model can be improved when processing massive data. In the experimental part, we conduct a detailed evaluation of the constructed natural language generation system using large-scale real-world data sets, compare multiple benchmark models, and demonstrate significant effects on specific tasks such as news summary generation and conversation response generation. Experiments show that the natural language generation system based on BERT model effectively utilizes the advantages of big data while maintaining high language quality, and improves the relevance and accuracy of generated content. © 2024 ieee.

关键词： data accuracy

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Extending the Mochi Methodology to Enable Dynamic HPC data Services

Extending the Mochi Methodology to Enable Dynamic HPC Data S...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Dorier, Matthieu Carns, Philip Ross, Robert Snyder, Shane Latham, Rob Gueroudji, Amal Amvrosiadis, George Cranor, Chuck Soumagne, Jerome Argonne Natl Lab Lemont IL 60439 USA Carnegie Mellon Univ Pittsburgh PA USA Intel Corp Houston TX USA

ISBN: (纸本)9798350364613;9798350364606

High-performance computing (HPC) applications and workflows are increasingly making use of custom data services to complement traditional parallel file systems with fast transient data management capabilities tailored to application specific needs. In the Mochi project we provide methodologies and tools that enable rapid development of custom HPC data services, including a collection of composable software components that can be combined to build complex distributed data services. Our initial version of Mochi targeted data services deployed with static configurations with a fixed number of nodes and minimal fault tolerance. However, there is a growing need for dynamic services that can adapt while running ill response to changing workloads and system conditions. In this paper we present our work to extend the Mochi architecture to support the development of dynamic data services. We achieve this by providing new Mochi components that support unified bootstrapping and online reconfiguration, fault detection, monitoring, and consensus. We also provide a methodology for deriving service-wide resilience from the resilience of each of the service's components.

关键词： data service Elastic HPC Mochi

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Map-Reduce (Hadoop) based data Clustering for Bigdata: A Survey 1

Map-Reduce (Hadoop) based Data Clustering for BigData: A Sur...

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1st international conference on cognitive computing and Engineering Education, ICCCEE 2023

作者： Umbarkar, A.J. Jadhav, S.A. Sheth, P.D. Sathe, A.S. Walchand College of Engineering Maharashtra Sangli India

ISBN: (纸本)9798350332803

data clustering problems are challenging due to bigdata. The volume, value, variety, velocity, veracity and structure of data makes clustering more complex. Processing time for data mining is directly proportional to the data to be processed. To access big data efficiently, data clustering may be a good solution. Traditional data clustering algorithms have been considered as NP-Hard problem. The clustering of big data also comes under the roof of the NP-Hard problem. Clustering algorithms require huge computations to make the cluster of data. An increase in data increases the number of computations. Hence, research in use of distributed computing for clustering algorithms and improvising resources utilization. However, distributed computing gives the worst results when data has dependencies. Distributed system like Hadoop run the clustering algorithms as the same code on partitioned data on different nodes of computing and finally gathers the result in one place. This paper gives a survey of Map-Reduce based big data clustering algorithms and problem solved. © 2023 ieee.

关键词： Big data

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