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

共 1359 条记录，以下是461-470 订阅

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Educational Missions and Tactics in the EU Artificial intelligence Strategy in the Era of Digital Transformation: A Policy Analysis Based on EU Documents Published During 2018-2022 3

Educational Missions and Tactics in the EU Artificial Intell...

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3rd international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Xiong, Yong Ling, Que Liu, Jinghua College of International Education Wenzhou University Zhejiang Wenzhou325035 China Institute of Education Xiamen University Fujian Xiamen361005 China

ISBN: (纸本)9781643684581

In the era of digital transformation, AI, cloud computing, big data, blockchain, and other intelligent technologies have increasingly profound impacts on work, study, life, and especially education. The Publishing of The Age of Artificial intelligence: Towards a European Strategy for Human-Centric Machines marked the official launch of the EU Artificial intelligence Strategy. By analyzing policy documents published during 2018 and 2022 for the EU Artificial intelligence Strategy, it is found that the strategy has set three strategic goals, namely achieving an 'Ecosystem of Excellence', building an 'Ecosystem of Trust', and establishing an 'Ecosystem of Digital Education'. To ensure its implementation, the European Commission has published a series of policy documents successively. As the main driver of the AI strategy, education plays an important role in the development of AI, and the EU has adopted various education tactics in the series of documents. Given the particularity of the EU as a regional cooperation organization, this strategy values philosophy, norms, and cooperation. It also provides a good reference for AI development and digital transformation of society in other countries and brings new opportunities for international cooperation in the field of AI. © 2023 The Authors.

关键词： Metadata

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Formal Verification of Path Planning Safety and Reachability in Unmanned Surface Vehicles 20th

Formal Verification of Path Planning Safety and Reachability...

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20th international conference on Intelligent computing (ICIC)

作者： Lu, Yu Sun, Pan Shi, Shijie Li, Meng Ge, Huilin Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China Shenzhen Univ Coll Appl Sci Shenzhen Peoples R China Jiangsu Univ Sci & Technol Ocean Coll Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9789819756742;9789819756759

In the realm of maritime technology, Unmanned Surface Vehicles (USVs) are increasingly crucial, particularly in their use for complex tasks requiring efficient and reliable path planning. This study addresses the critical intersection of path planning and energy efficiency in USVs, focusing on the challenge of navigating dynamic oceanic environments while minimizing energy consumption. Building upon our previous work, which developed an energy-efficient path planning algorithm incorporating the Voronoi diagram, Visibility graph, Dijkstra's search, and an energy consumption function, we extend our research to the formal verification of these path planning algorithms. We employ model checking as a robust formal verification method. This paper presents a novel framework for verifying key correctness properties of USV path planning, including deadlock-freeness, safety, reachability, and energy efficiency, using linearly priced time automata (LPTA) and the UPPAAL model checking tool. Our approach not only demonstrates the potential of model checking in enhancing the reliability and operational viability of USVs but also pioneers its application in validating the real-world efficacy of USV path planning algorithms. The findings offer significant insights for advancing safer, more efficient, and economically viable USV operations, setting a new benchmark in the field of maritime autonomous systems.

关键词： USVs Formal verification Path planning

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A Fine-grained Multi-label Privacy Detection Model for Unstructured data Based on BERT Pre-training 24

A Fine-grained Multi-label Privacy Detection Model for Unstr...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Hu, Yuchuan Guo, Bin Dai, Cheng Zheng, Qin Weng, Fangpeng Li, Zhongwei Sichuan University Computer Science Department Chengdu China

ISBN: (纸本)9798350319934

At present, a major bottleneck in the development of artificial intelligence is the difficulty of training data collection. data holders are reluctant to share data because of concerns that sharing data may reveal private information. In order to solve this problem, this paper proposes a fine-grained multi-tag privacy detection model for unstructured data based on BERT pre-training. The model extracts the feature representation at the sentence level through BERT, and then allocates the weight to each sentence through the attention mechanism at the document level to obtain the feature representation of the document. Finally, combined with the calculation results of tag relevance based on GCN network, the result of multi-tag classification of unstructured text is obtained. This model achieves good results in the data set used in this paper. © 2022 IEEE.

关键词： Classification (of information)

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A Smart Approach using Multi-agent System for big data Security 9

A Smart Approach using Multi-agent System for Big Data Secur...

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9th IEEE international conference on Internet of Things - Systems, Management and Security (IOTSMS)

作者： Kassimi, Dounya Kazar, Okba Barka, Ezedin Merizig, Abdelhak Houhamdi, Zina Athamena, Belkacem Zaoui, Meftah Univ Ctr Aflou Dept Comp Sci Laghouat Algeria UAE Univ Dept Informat Syst & Secur Al Ain U Arab Emirates Univ Biskra Dept Comp Sci Biskra Algeria Al Ain Univ Cybersecur Dept Al Ain U Arab Emirates Al Ain Univ Dept Business Adm Al Ain U Arab Emirates

ISBN: (纸本)9798350320459

With the evaluation of technology and the appearance of new tools that help store the information we create, especially in Banking, business intelligence, and even Education as datawarehouse, big data, and cloud computing. Those new tools create another obstacle: how we can secure and protect the information and the data stored in them. This paper treats the problem of big data security and privacy using mobile and stationary agents' technologies. The main important security proprieties in big data are integrity, authentication, privacy, and access control. For integrity, the problem lies in checking the integrity of data and if this data is good and can be used since the big data is receiving data from different sours and different formats (structured, semi-structured, and non-structured). In access control, we need to consider the users' secrecy, monitor the authorities, and properly apply the confidentiality requirement. The problem with authentication is to authenticate each user over the network, while big data collect sensitive data from trusted or untrusted users. While the privacy policy problem revolves around data collection and the use of transparency. To answer the previous needs for security in big data, we have proposed a smart approach using multi-agent systems (MAS) as a model for our solution.

关键词： big data security and privacy multi-agent system mobile agent Hadoop Pentaho

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Mobility-Aware Serverless Function Adaptations Across the Edge-Cloud Continuum 15

Mobility-Aware Serverless Function Adaptations Across the Ed...

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IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on big data computing, Applications and Technologies (BDCAT)

作者： Raith, Philipp Rausch, Thomas Dustdar, Schahram Rossi, Fabiana Cardellini, Valeria Ranjan, Rajiv TU Wien Distributed Syst Grp Vienna Austria Univ Roma Tor Vergata DICII Rome Italy Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781665460873

Serverless functions have emerged as a useful abstraction to manage the complexity of distributed and heterogeneous edge-cloud infrastructure. Current cloud-centric orchestration services and serverless platforms are not suitable for the edge-cloud continuum due to their mobility-unawareness. In this paper, we present a mobility-aware framework for edgecloud systems, where the operational mechanisms of placement, scaling, and routing of serverless functions work in tandem. To that end, we formulate the concept of pressure that captures complex system behavior in a single metric. The pressure-based framework handles geo-distributed workload and user mobility by reducing overall function latency and increasing data throughput while efficiently using edge resources. Our contributions include a novel framework revolving around pressure and a realworld proof of concept evaluation. Our approach combines the benefits of a centralized control-plane with a decentralized dataplane. The results show the efficacy of the platform to address operational goals and make effective and deterministic tradeoffs between system utilization and application performance. The novel concept of pressure shows great extensibility and builds the base for a plethora of future works.

关键词： Edge Cloud Continuum Serverless computing Function as a Service Edge intelligence

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Performance Analysis of big data Motifs on Large Core Machines 11

Performance Analysis of Big Data Motifs on Large Core Machin...

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11th IEEE international conference on Cloud computing in Emerging Markets, CCEM 2022

作者： Bhat, Kunal Nai, Kartika Shetty, Nihal Nagarajan, Yashaswini Kalambur, Subramaniam PES University Department of Cse Bengaluru India

ISBN: (纸本)9798350394214

The last decade has seen improvements in processor architecture that have enabled CPU dies to pack 64 to 128 cores, compared to 4 to 8 cores only a few years ago. Applications are being developed to take advantage of multi-core, high-performance computing. As a result of the volume of applications being developed, particularly in the big data and Artificial intelligence fields, it is not possible to benchmark every single one of them. To tackle this problem, data motifs may be used. data motifs can be defined as core units of computation that can be distinguished from other computational units by their data access patterns. Essentially, the most time consuming part of a program can be considered a motif. A program can be modelled as a pipeline of many motifs. As development paradigms shift towards making use of large core computing, which is defined in this paper as 16 or more cores, data access patterns for programs tend to change. As with most multi-threaded programs that follow Amdahl's and Gustafson's laws, there is a certain limit to the speedup that can be achieved, before synchronization begins to overbear the time saved through parallelism. Synchronization may consist of access to shared data and locking, as well as lower level mechanisms such as cache contention. The aim of this research is to corroborate the existence of data motifs through analysis of run-time data, analyze the performance of these data motifs on large core machines, and provide an understanding of how data access patterns vary with the underlying software stack. This paper details the results of the subsequent analysis and comparison of the motifs based on the shared memory reads and writes between their implementations. © 2022 IEEE.

关键词： big data

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Innovative Digital Twin Platform Construction for Smart Grid System 23

Innovative Digital Twin Platform Construction for Smart Grid...

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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： Zhong, Yiguo Zhang, Wei Ha, Xueping Chen, Qinghai Huang, Jing Yan, Ke Hangzhou Gisway Information Technology Co. Ltd Hangzhou China

ISBN: (纸本)9781665494571

Digital twin platform that builds digital models of the buildings and infrastructures plays an important role in smart city design, construction and maintenance. The digital twin models are capable of utilizing artificial intelligence (AI) and information technologies, such as perception, calculation and modeling to describe, diagnose, predict and make decisions on the cyber-enabled world based on the big data collected by various sensor networks. In this study, an innovative digital twin platform is designed and implemented for a smart grid system and grid substation in Zhejiang, China. The proposed digital twin model improves the original traditional operation and maintenance of the smart grid system into an intelligent grid management system. The management, operation, and maintenance capabilities of the substation are improved. The proposed method achieves the purpose of improving business efficiency, reducing overall costs, and creating business value. © 2021 IEEE.

关键词： Substations Smart cities Maintenance engineering Predictive models data models Digital twins Smart grids

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IIoT Mobile Business data Placement Strategy Based on Bayesian Optimization Algorithm 24

IIoT Mobile Business Data Placement Strategy Based on Bayesi...

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作者： Shen, Zhijie Liu, Bowen Xu, Xiaolong Qi, Lianyong Dai, Fei Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Shenzhen China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Qingdao China School of Computer Science Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350319934

As a standardized and systematic management method, business process management plays an important role in the industrial Internet of Things (IIoT) scenario. With the rapid development of mobile Internet and the increasing amount of data generated by the Internet of Things devices, the traditional storage scheme can no longer meet the requirements of managing the IIoT mobile business data. The cloud-edge collaborative data storage scheme provides mass storage capability by the cloud data center, and meanwhile takes access delay into account by storing data in the edge servers near to user, making it an effective solution to manage mobile business data in the IIoT scenario. However, the storage capacity of edge servers is relatively limited. How to design a reasonable data placement strategy to maximize the speed of data reading and tasks execution in IIoT business process has become an urgent problem to be solved. In order to shorten the overall running time, reduce data transmission costs and enhance users experience, we propose a data placement strategy based on Bayesian optimization algorithm, which can reasonably allocate storage capacity in the edge side, and further determine which edge server the data should be placed on. The results of the experiment show that the strategy based on our proposed algorithm performs better in a variety of different scenarios than the ones that based on other kind of optimization algorithms which have been adopted in similar studies. © 2022 IEEE.

关键词： Edge computing

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Energy Efficient Smart Wearables for the Advance Ubiquitous Applications in the era of Smart Health using big data

Energy Efficient Smart Wearables for the Advance Ubiquitous ...

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2022 international conference on Augmented intelligence and Sustainable Systems, ICAISS 2022

作者： Kaur, Upinder Murali Dhar, M.S. Reka, M. Vidya Priya Darcini, S. Radhika Patnala, Tulasi Khan Patan, Muzeeb Akal University Dept. of CSE Sabo Punjab Talwandi India Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India Sona College of Arts and Science Dept. of CSE Salem India Sri Ramakrishna College of Institute and Technology Dept. of CSE Coimbatore India GITAM Univercity Dept. of ECE Hyderabad India Sagi Rama Krishnam Raju Engineering College Dept. of ECE Bhimavaram India

ISBN: (纸本)9781665489621

For the IoT to succeed, every conceivable item must be linked together (healthcare sensors, cameras, household appliances, smart phones, wearable sensors, etc.). As a result, dependable ways of data storage and processing are required to cope with the vast amounts of information created by all of these channels. Cloud computing is a new method being developed to solve this issue. However, some applications (healthcare) necessitate minimal latency and delay since they must process data in real time to increase performance. One of the promising technologies that help the healthcare sector is fog computing since it helps with things like multihop data connection delays, resource distribution, and service flexibility. This research proposes a fog-based IoT healthcare system to reduce the power needs of individual fog nodes. The experimental outcomes demonstrate the effectiveness of the suggested framework with regards to network delay and power consumption. In addition, the authors discussed and made suggestions for key services of big data infrastructure that should be available in fog devices for healthcare big data analytics. © 2022 IEEE.

关键词： Fog

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Hybrid Cat-Artificial Fish Swarm Based Node Deployment Optimization in Intelligent Transportation IoT 19

Hybrid Cat-Artificial Fish Swarm Based Node Deployment Optim...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Yi, Lingzhi Luo, Bin Zhu, Chenlu Deng, Xianjun Xia, Yunzhi Wu, Hengshan Zhongnan University of Economics and Law School of Information and Safety Engineering Wuhan430073 China University of South China School of Civil Engineering Hengyang421001 China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Hubei Chutian Expressway Digital Technology Co. Ltd Wuhan430074 China

ISBN: (纸本)9798350346558

The integration of Internet of Things (IoT)-based highway bridge structural health monitoring (HBSHM) and intelligent transportation systems (ITS) plays an essential role in detecting damage for large and complex bridge structures. The HBSHM provides ITS with bridge health condition information for real-time traffic management. The ITS provides HBSHM with the required traffic data to improve the HBSHM system's accuracy and reliability. Node deployment optimization (NDO) is critical in HBSHM as sensor node deployment directly affects the quality of collected data. To better solve the NDO problem in HBSHM, a novel hybrid cat-artificial fish swarm algorithm (CAT-AFSA) based on the Artificial Fish Swarm Algorithm (AFSA) and Cat Swarm optimization (CSO) is proposed in this paper. The algorithm introduces seeking and tracing behaviors of CSO into AFSA, which effectively expands the search space of artificial fish and reduces the blind search of it. The performance of the CAT-AFSA is first tested using four benchmark functions, and then its validity and feasibility for the NDO are verified using an engineering example. The simulations demonstrate that the CAT-AFSA has better search performance and computational efficiency compared with the AFSA and CSO. The CAT-AFSA has a broad application prospect in the field of NDO in civil engineering. © 2022 IEEE.

关键词： Intelligent systems

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