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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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Construction of user Portrait System Based on information warehouse under big data 2

Construction of user Portrait System Based on information wa...

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

作者： Fang, Yaxin Liu, Zhonglin Li, Changbao Pan, Shuang North China Institute of Computing Technology Big Data Analysis Department Beijing100083 China

ISBN: (纸本)9783800760268

With the continuous development of Internet technology, the scale of data volume shows an explosive growth *** this case, the traditional user portrait system will have problems such as insufficient data storage and slow calculation. Based on this, this paper designs a user portrait technology based on information warehouse under big data. Through the data acquisition module, the distributed information collection is carried out to complete data acquisition, data cleaning and zoning. Through the data warehouse hierarchical operation, the data is accumulated and summarized, and complex tasks are resolved into multiple simple tasks. It is convenient to locate problems, improve the reusability of calculation results, and decouple original data and statistical data. Through user portrait management platform module, the labels are classified, customized[For the labels of statistical classes are distributed and aggregated through spark. For the labels of mining classes, K-means algorithm is used to build a multi-level user portrait model. The storage of portrait data and the screening of multi label combination are realized through the user clustering module. The experimental results show that the system is suitable for the construction of user portrait in the case of large amount of data[The advantages of the system are lower delay and stable accuracy. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： big data

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Research on full-process product defect traceability analysis technology based on workshop big data 2

Research on full-process product defect traceability analysi...

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2nd international conference on Algorithms, High Performance computing and Artificial intelligence, AHPCAI 2022

作者： Zhang, Jianquan Lan, Shiqing Liu, Lei He, Ru Shenzhen Catic Information Industry Group Co. Ltd. Guangdong Shenzhen518055 China IoT Research Institute Shenzhen Polytechnic Guangdong Shenzhen518055 China

ISBN: (纸本)9798350334784

The Internet of Things(IOT) technology collects real-time data from material management, equipment status, production process integration, process parameters and other accurate traceability analysis, obtains the information of the whole production process, optimizes the production plan and workshop scheduling, and promotes the intellectualization of the manufacturing industry in the digitization of the whole production process. In the production process of modern discrete manufacturing industry, various types of data will be continuously generated. Radio Frequency Identification(RFID), industrial wireless sensor network, Ethernet and intelligent gateway equipment will be used to realize interconnection and intercommunication. The product quality data in the production process will be collected automatically and in real time. The data will be returned to the data integration center in the form of sensor network networking and stored in the big data platform system, and the big data analysis will be applied, Mining, forecasting, analyzing and processing the product quality, and combining scientific decision-making methods to optimize the production process and achieve more rapid and accurate control. This paper analyzes the structure of industrial big data platform, gives the process of product quality prediction based on big data, and designs the discrete manufacturing product quality prediction model to realize the regression prediction of product quality based on eXtreme Gradient Boosting(XGBoost). © 2022 IEEE.

关键词： Internet of things

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Proceedings - 2022 international conference on Information System, computing and Educational Technology, ICISCET 2022

Proceedings - 2022 International Conference on Information S...

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2022 international conference on Information System, computing and Educational Technology, ICISCET 2022

ISBN: (纸本)9781665460446

The proceedings contain 80 papers. The topics discussed include: research on the influence of big data information processing technology on English oral anxiety;operation design of comprehensive scoring system for classical dance training based on information technology;application of data mining technology of data fusion information entropy in scientific and technological cooperation;research on the influence of internalization of extrinsic motivation in English learning based on artificial intelligence assisted technology;English education software assisted teaching based on big data;research on business data management system of cultural and creative product design and development based on artificial intelligence algorithm;evaluation model of teaching quality based on algorithm of stepwise regression analysis;design of college English teaching computer aided system based on dm technology;research on the strategy of Chinese language teaching system in colleges and universities based on the background of VR technology;and virtual reality technology based simulation training system research of pan business English activities.

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Resource-Efficient DNN Training and Inference for Heterogeneous Edge intelligence in 6G 23

Resource-Efficient DNN Training and Inference for Heterogene...

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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： Cui, Enfang Zhang, Weiting Yang, Dong Wu, Wen Lyu, Feng School of Electronic and Information Engineering Beijing Jiaotong University Beijing China University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665494571

Edge intelligence is expected to be a key enabler of the future sixth generation (6G) mobile network. However, the heterogeneous characteristics of edge intelligence, such as heterogeneous edge data, resources, and service requirements, pose challenges to deep neural network (DNN) training and inference at the edge. To fully tap the potential of DNN for 6G heterogeneous edge intelligence, a variety of solutions have been proposed to tackle the above challenges, such as federated learning and transfer learning, for DNN training;and DNN partitioning and early exiting, for DNN inference. In this paper, we provide a comprehensive survey about resource-efficient DNN training and inference in heterogeneous edge intelligence. We first discuss the challenges of DNN training and inference in heterogeneous edge intelligence. Then, we give a detailed review of the recent advances of DNN training and inference technologies in heterogeneous edge intelligence, including the principles and state-of-the-art solutions of these technologies. We further conduct a taxonomy and summary of the reviewed solutions to clarify their applicable scenarios and the limitations. Finally, we point out some potential future research opportunities on heterogeneous edge intelligence. © 2021 IEEE.

关键词： Training 6G mobile communication Deep learning Smart cities Taxonomy Transfer learning Neural networks

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Partial Deep Domain Adaptation for Non-Intrusive Load Monitoring 21

Partial Deep Domain Adaptation for Non-Intrusive Load Monito...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Yuying Yang, Yang Wang, Chen Liu, Runqin Jiang, Hongbo Liu, Wenping Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China Huazhong University of Science and Technology Wuhan China China Unicom Central South Research Institute Changsha China Hunan University Changsha China Hubei University of Economics Wuhan China

ISBN: (纸本)9798350329223

With the widespread deployment of smart meters and the burgeoning integration of the Ubiquitous Power Internet of Things (UPIoT), Non-Intrusive Load Monitoring (NILM) has emerged as an essential technology, providing granular insights into device-level energy consumption. However, NILM model generalization is impeded by user and appliance heterogeneity. Domain adaptation has been leveraged to enhance NILM models, but it typically necessitates consistent appliance categorization across households. Addressing this, our study presents Weighted Adaptation NILM (WeAd-NILM), a partial deep domain adaptation model designed for Partial Domain Adaptation (PDA) scenarios that accommodate diverse appliance categories, aligning with real-world load decomposition tasks. WeAd-NILM introduces a sample weighting mechanism tailored for PDA, mitigating negative transfer from outliers. Through distribution analysis and multi-objective regression, WeAd-NILM effectively performs load decomposition, fostering knowledge transfer. Verified on UK-DALE and REFIT datasets, WeAd-NILM demonstrates exceptional transferability and load decomposition accuracy. © 2023 IEEE.

关键词： Internet of things

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Deep learning algorithms driven by artificial intelligence technologies may cause the disruption of information interaction mechanisms and the creation of technological monopolies

Deep learning algorithms driven by artificial intelligence t...

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2022 international conference on Electronic Information Engineering, big data, and Computer Technology, EIBDCT 2022

作者： Liu, Jing Law School Xi'an Jiaotong University 28 Xianning West Road Shaanxi Province Xi'an China

ISBN: (纸本)9781510655133

After deep learning algorithms were proposed, artificial intelligence technology applications have achieved breakthrough development. Currently, the explosive growth of data provides sufficient nutrients for artificial intelligence, and deep learning algorithms have achieved breakthroughs in speech and visual recognition, making it possible for artificial intelligence industries to land and commercialize. With the continuous development of big data, deep learning and cloud computing, we can obtain more and richer data, develop more efficient algorithms, and have more powerful computing power, laying the foundation for another artificial intelligence research boom. However, with the application of AI-related technologies in industries such as information, social governance, and transportation, the problems and challenges of algorithmic collusion and algorithmic discrimination have gradually emerged. The operating principles of algorithms differ from the risk of algorithmic collusion that may result, and they also pose different degrees of regulatory challenges for antitrust enforcement. By understanding the data-driven competitive model of the market under the influence of algorithms, the efficient information interaction mechanism and the new features embodied in the evolution of machine-driven competition can prevent the breeding of technological monopolies. The impact of algorithms on the collusion problem has two dimensions: the first dimension is to change the environment of collusion;the second dimension is to be applied directly as a tool in the collusion process. This article attempts to analyze the challenges brought to modern market competition by exploring the state of artificial intelligence technology that causes data monopoly. Thus, In order to better regulate the evaluation and regulation of artificial intelligence algorithm collusion, this article proposes related solutions based on the Chinese perspective, including: 1) broaden the extension of the c

关键词： Learning algorithms

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Research on Malware Detection System Using Artificial intelligence 7

Research on Malware Detection System Using Artificial Intell...

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

作者： Kim, MinSu Joongbu Univ Department of Information Security Engineering Seoul Korea Republic of

ISBN: (数字)9781665465823

ISBN: (纸本)9781665465823

Malicious code is a program that accesses and destroys a system or performs malicious actions such as leaking information. Typically, viruses, Trojan horses, and worms attack the system by hiding malicious code through the same path as e-mail. As modern technology develops, the technology of malicious code is also becoming more sophisticated and diversified. Many malware creators distribute malware for the purpose of gaining financial gain. In order to achieve its purpose, it is necessary to produce the maximum effect with minimum cost and effort, so rather than elaborately produced, malicious programs with similar functions are printed quickly. In addition, there are cases where the malicious code of the creators for political purposes and the creators for the display of technology is elaborately created for the purpose of system vulnerabilities and zero-day attacks, so that it is impossible to detect them. As the core technology of the 4th industrial revolution and the technology that incorporates artificial intelligence, which lies at the center, has emerged as a core industry, major countries and many companies around the world are making intensive investments and RAD in artificial intelligence, there is. As artificial intelligence technology is being researched and invested in various fields, research and development of artificial intelligence technology is in progress in the information security industry. In line with this trend, we plan to effectively respond to malware by applying artificial intelligence to the detection of various and sophisticated malicious codes. © 2022 IEEE.

关键词： Decision trees

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Research on ERP Intelligent Human Resource Management System Based on Computer big data Technology

Research on ERP Intelligent Human Resource Management System...

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2022 international conference on data Analytics, computing and Artificial intelligence, ICDACAI 2022

作者： Liang, Ding Wu, Yu Wanjiang University of Technology Anhui Ma'anshan China

ISBN: (数字)9781665454704

ISBN: (纸本)9781665454704

ERP system has essential application value in enterprise human resource management. The screening and adjustment of various personnel data in human resource management are directly affected by data management. The extensive application of ERP human resource management system in enterprise human resource management has effectively improved the quality and efficiency of enterprise management of employees. The system function of ERP integrates human resource management into it and expands the overall and whole-process control management of ERP. This paper analyzes the design and construction of the human resource management ERP information system in detail. Finally, a set of perfect human resource management information systems is designed according to the enterprise's situation. © 2022 IEEE.

关键词： Human resource management

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Attribute Reduction Method Based on Improved Granular Ball Neighborhood Rough Set 7

Attribute Reduction Method Based on Improved Granular Ball N...

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

作者： Xia, Han Chen, Zizhong Wu, Yanmin Qi, Jinli Chongqing Key Laboratory of Computer Science and Technology Chongqing University of Posts and Telecommunications ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China ChongQing College of Electronic Engineering Department of General Education and International Studies ChongQing China

ISBN: (纸本)9781665487115

Feature reduction is an important aspect of big data analytics today, and neighborhood rough set is a classic attribute reduction method. The traditional neighborhood rough set finds out the radius suitable for problem solving by specifying the radius of the neighborhood or using a grid search method. There are a lot of overlapping calculations in the neighborhood calculation, and the calculation complexity is high. The granular ball neighbor-hood rough set can adaptively generate different neighborhood radius, and obtains better algorithm efficiency and learning effect than the traditional neighborhood rough set. However, due to the overlap problem in the process of granular ball generation, the granular ball neighborhood rough set still exists the overlap calculation of neighborhoods. For this reason, this paper solves the problem of granular ball overlap by detecting heterogeneous ball overlap and splitting, and thus designs an improved granular ball neighbor-hood rough set attribute reduction method. Experiments show that the improved granular ball neighborhood rough set has higher classification accuracy on the public benchmark dataset than the classic neighborhood rough set and the standard granular ball neighborhood rough set. © 2022 IEEE.

关键词： Cloud computing Costs Search methods Rough sets big data Benchmark testing Classification algorithms

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