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Digital course scheduling system for data-driven industrial worker training based on 5G enabling environment

Digital course scheduling system for data-driven industrial ...

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2024 international conference on Artificial intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Chen, Yuzhong Zhong, Yue Guangdong Construction Polytechnic Guangdong511500 China

ISBN: (纸本)9798331520816

5G wisdom on the basis of digital campus, campus under the effective use of 5G technology of Internet of things, cloud computing, big data and other advanced information engineering to carry out a full range of campus construction, promoting the scientific decision-making, build open teaching environment, good for all the teachers and students to provide more convenient and comfortable place to live, work, study[1]. This article mainly around college students online course management system implementation technology selection, system requirements, database design and so on carries on the analysis, thus the college students' course selection management system based on B/S structure, B/S architecture to implement development, convenient maintenance, and to implement low cost price, so as to achieve high efficiency low cost development, the main functional modules of the system are: Student and teacher information management, user rights control, course management, classroom management, class management, course selection management, class withdrawal, course selection statistics management, students, teachers and school administrators only need to click on the browser to access the system can carry out the corresponding course selection and system management and maintenance, Java programming language and popular frameworks such as Spring Boot, Bootstrap and some Ajax technologies are selected to achieve the smoothness of data interaction at the front and back end of the system. With the advent of the Internet era, the birth of Internet +[2], which can be added to a large number, gradually adds every bit of life, there is no doubt that education has always been a hot issue in the world. © 2024 IEEE.

关键词： Java programming language

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Time Labeled Visibility Graph for Privacy-Preserved Physiological Time Series Classification 7

Time Labeled Visibility Graph for Privacy-Preserved Physiolo...

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

作者： Xiu, Yuxuan Ren, Xinyue Zhang, Ting Chen, Yanyu Jiang, Li Li, Duo Wang, Xingjun Zhao, Liang Chan, Wai Kin Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University Department of Electronic Engineering Shenzhen518055 China

ISBN: (纸本)9781665487115

With the development of artificial intelligence (AI) and the continuous improvement of medical informatization, health assessment and auxiliary diagnosis based on physiological time series has become a hot research topic. However, the direct use of raw time series data is inappropriate due to privacy protection regulations in medial scenarios. Therefore, we designed a privacy-preserved framework based on Visibility Graph (VG) transformation and Graph Neural Network (GNN) for physiological data multi-classification. In particular, we proposed a Time Labeled Visibility Graph (TLVG), which uses the idea of VG to protect privacy while retaining more information that is useful for classification. Experiments are conducted based on the ECG5000 electrocardiogram dataset of the UCR time series classification archive. The comparison with existing classic and transformation-based classifiers shows the effectiveness and stable performance of our proposed method, providing an alternative and reasonable way for diseases diagnosis decision supports. Furthermore, from this research, it is discovered that the time sequence of each node in VG is an important feature in time series classification tasks. © 2022 IEEE.

关键词： Privacy data privacy Time series analysis Physiology Regulation Graph neural networks Medical diagnosis

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Research on Face Detection and Recognition with Face Mask Based on FaceNet 4

Research on Face Detection and Recognition with Face Mask Ba...

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4th international conference on Robotics, Intelligent Control and Artificial intelligence, RICAI 2022

作者： Wang, Hongxia Gu, Yuting Li, Hui School of Big Data and Artificial Intelligence Anhui Xinhua University Heifei China

ISBN: (纸本)9781450398343

According to the requirements of face recognition with masks in epidemic environment, this paper analyzes the difficulties of face recognition with masks, and puts forward a scheme combining face detection, data enhancement and face recognition. Firstly, face detection is performed by MtCNN algorithm;Then, data enhancement technology is used to enhance the complexity of sample data sets and further optimize FaceNet model. Finally, the face feature vector is extracted by FaceNet model to realize the correct recognition of mask face. Experiments show that this scheme can improve the accuracy of face recognition with masks to a certain extent, and at the same time improve the traffic speed. © 2022 ACM.

关键词： Face recognition

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2022 international conference on Innovation and intelligence for Informatics, computing, and Technologies, 3ICT 2022

2022 International Conference on Innovation and Intelligence...

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2022 international conference on Innovation and intelligence for Informatics, computing, and Technologies, 3ICT 2022

ISBN: (纸本)9781665451932

The proceedings contain 116 papers. The topics discussed include: detection of sesame disease using a stepwise deep learning approach;application of industrialized building system in the field of construction toward sustainability: future routes and a theoretical mapping;scheduling household appliances using genetic algorithms;empirical study for improving project allocation on freelancing platform;an estimation of the multidimensional energy poverty index in Pakistan;machine learning and big data based ids system extensive survey;damage assessment and recovery in fog-based computing systems;trust-aware security system for dynamic southbound communication in software defined network;an efficient approach to reduce the encryption and decryption time based on the concept of unique values;reconfigurable cyber-security architecture for small satellite with low complexity and power;developing modern agent technologies in combating COVID-19 exposure: an application in a healthcare facility;ease of access to healthcare services : developing a telehealth application prototype;and polymer optic fibers: potential of development and application in networks.

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An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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Morphological Guided Causal Constraint Network for Medical Image Multi-Object Segmentation

Morphological Guided Causal Constraint Network for Medical I...

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Gao, Yifan Li, Jun Chang, Xinyue Zhang, Yulong Chen, Riqing Yang, Changcai Wei, Yi Dong, Heng Wei, Lifang Fujian Agriculture and Forestry University College of Computer and Information Science Fujian China Nanjing University of Information Science and Technology Institute for Ai in Medicine School of Artificial Intelligence Nanjing China Fujian Medical University College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics Fujian China Fujian Agriculture and Forestry University Digital Fujian Research Institute of Big Data for Agriculture and Forestry Fujian China Wuyi University School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Fujian China

ISBN: (纸本)9798350337488

Multi-objective segmentation (MOS) in medical images is to simultaneously extract multiple regions of interest in the medical images. Due to the unbalanced distribution of samples and the similarity and significant differences between features in medical images, current methods still struggle to achieve satisfactory results. In this context, we propose a novel Morphological Guided Causal Constrain segmentation network (MCCSeg) for medical image multi-object segmentation. We introduced a Causal Constrain Module (CCM) for feature decorrelation by sample reweighting. The morphological guidance module (MG) is designed to extract the boundary features as the prior shape information for enhancing feature representation. Our experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively. © 2023 IEEE.

关键词： causal learning deep learning Medical image morphological guidance multi-object segmentation

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Rough Sets Turn 40: From Information Systems to Intelligent Systems 17

Rough Sets Turn 40: From Information Systems to Intelligent ...

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17th conference on Computer Science and intelligence Systems (FedCSIS)

作者： Skowron, Andrzej Slezak, Dominik Polish Acad Sci Syst Res Inst Warsaw Poland Cardinal Stefan Wyszynski Univ Warsaw Poland Univ Warsaw Inst Informat Warsaw Poland QED Software Sp Zoo Warsaw Poland

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

The theory of rough sets was founded by Zdzislaw Pawlak as a framework for data and knowledge exploration. His seminal paper titled "Rough Sets" was published in 1982, in international Journal of Computer and Information Sciences. One of the key aspects that lets us use rough sets in practical scenarios is the notion of information system, which comes from even earlier Professor Pawlak's works. Information systems are the means for data and knowledge representation. They constitute the input to rough set mechanisms aimed at computing approximations of concepts and deriving compacted, interpretable decision models. In particular, the fundamental notion of the indiscernibility relation is defined on the basis of a given information system. Accordingly, we discuss to what extent information systems can serve as the basis for intelligent systems. We claim that in many cases it is not enough to treat a data set - represented as an information system - as a purely abstract object with no linkage to the data origins. Oppositely, we should give ourselves a technical possibility to construct information systems dynamically, taking into account interaction with physical environments where the data comes from. With this respect, we refer to the notions of interactive granular computing and we generally consider together the paradigms of rough sets, information systems, and information granulation.

关键词： Rough Sets Information Systems data Mining big data Interactive Granular computing Intelligent Systems

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Distributed Architecture of an Intrusion Detection System in Industrial Control Systems 14th

Distributed Architecture of an Intrusion Detection System in...

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14th international conference on Computational Collective intelligence (ICCCI)

作者： Abid, Ahlem Jemili, Farah Korbaa, Ouajdi Univ Sousse MARS Res Lab LR17ES05 ISITCom H Sousse 4011 Tunisia

ISBN: (纸本)9783031162107;9783031162091

Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, big data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. To improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods including machine learning, big data techniques and deployed in a cloud environment. We use the industrial dataset SWat for the experiment. Our system achieved good results in terms of Accuracy (99%) and response time by using Gradient-Boosted Trees (GBTs) classifier due to the performance of databricks and Apache Spark.

关键词： Intrusion detection Industry 4.0 Industrial control systems Artificial intelligence Machine learning Cloud computing

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Research on the Funding Effectiveness of National Major Scientific Research Instrument Development Projects Based on Literature big data Analysis 6

Research on the Funding Effectiveness of National Major Scie...

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6th international conference on Algorithms, computing and Artificial intelligence, ACAI 2023

作者： Liu, Yitong Shang, Xueshen Li, Xiaomeng Yang, Caijun Wang, Lili Zhang, Yu Pan, Feng Li, Liu Literature Library National Institute of Metrology China

ISBN: (纸本)9798400709203

Scientific research instruments are the basic conditions and achievements of scientific and technological innovation. Under the situation of a new round of scientific and technological revolution and industrial reform, they have become the focus of scientific and technological innovation competition in the world, including China. Taking the scientific research output in the National Major Scientific Research Instrument Development Project (NMSRIDP) as the research object, this paper used bibliometric analysis to reveal the quantity, quality characteristics and internal laws of scientific papers from different dimensions, such as the output quantity, published journals, citation frequency, output institutions, etc. At the same time, this paper analyzed the funding effectiveness of instrument development projects from 2011 to 2022 in terms of output, output influence, input-output ratio and other dimensions. It is expected to provide a basis for the performance evaluation of the achievements and outputs of science fund projects, and provide reference value for understanding the role of instrument fund in promoting the frontier breakthrough of basic research in China. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： big data

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Research on university ranking based on literature big data and industry thesaurus - Taking double first-class universities as an example 5

Research on university ranking based on literature big data ...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Fan, Yuhang Wang, Xiaoxia Chen, Miao Zhu, Yue Xu, Pengjing Shanghai Technology Development Co. Ltd. Shanghai200062 China Data Science and Artificial Intelligence Institute of Shanghai Science and Technology Innovation Resources Center Shanghai200062 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Most of the current university rankings base on the academic system and lack the detailed classification. It is not that suitable for industry field to screen the universities for the potential corporation on research and development. In this paper, we adopt big data processing technology to form a structured process of university rankings based on the industrial system, with support on the massive literature data and the built industry thesaurus from our previsions study. It consists of four modules: resource download, text segmentation, data processing and result presentation. Results show that our new ranking can reach the precise divisions on subdivided industry, also in accord with other authoritative rankings in the similar divisions from both classifications. Our ranking can possibly help understand the research priorities of different universities in perspective, improve the efficiency of technology transfer from universities to industry and sketch the map of industry-university-research system. © 2022 IEEE.

关键词： big data

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