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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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Trustworthy Cloud Storage data Protection based on Blockchain Technology

Trustworthy Cloud Storage Data Protection based on Blockchai...

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2022 international conference on Edge computing and Applications, ICECAA 2022

作者： Jena Catherine Bel, D. Esther, C. Zionna Sen, G.B. Tamizhmalar, D. Dhinakaran, D. Anish, T.P. Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai India Department of Information Technology Velammal Institute of Technology Panchetti Chennai India Department of CSE R.M.K College of Engineering and Technology Chennai India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

A cloud-based repository is the most popular option for individuals and businesses to share data digitally. To move and store the data, conventional cloud hosting has come to rely on big storage providers functioning as reliable intermediaries. Although cloud providers offer robust security measures, rising usage and demand have made these centralized platforms prime targets for security breaches and hackers. This research study proposes a blockchain-based public key cryptosystem data protection scheme to address the problem. All of the data is automatically encrypted using the homomorphic technique. The ciphertext index Ci is created via a public key accessible crypto technique and set aside in the blockchain. At the same time, confidential information is protected and published in a cloud storage server. A reliable verification cloud server is used to access and manage data across users. The blockchain automatically syncs data transactions, which is more equitable to protect the confidentiality andintegrity of the shared data process. In the case of numerous users, the integration of distributed ledger technology and a dedicated cloud verification server makes it easier to implement a fair and reliable access control system. Finally, the authentication process and performance evaluation demonstrate the schemes ensure the efficiency. © 2022 IEEE.

关键词： Blockchain

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Application of Artificial intelligence in Human Resource Management of T Insurance Company in the Post-epidemic Era 4th

Application of Artificial Intelligence in Human Resource Man...

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4th international conference on Innovative computing, IC 2021

作者： Wang, Ji Hao, Yue College of Business Administration Jilin Engineering Normal University Jilin Changchun130012 China

ISBN: (纸本)9789811642579

With the advent of the post-epidemic era, human resource management is the most urgent issue faced by small and medium-sized entrepreneurs during the epidemic. This paper analyzes the influence of artificial intelligence technology on the human resource management of T Insurance company by using literature and market survey method. As an emerging technology field, the popularization and application of ARTIFICIAL intelligence technology will still face some difficulties. Finally, the paper puts forward specific strategies to promote the extensive application of artificial intelligence technology in human resource management in the post-epidemic era. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Human resource management

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Research on Array Circuit Design Based on In-Memory computing 4

Research on Array Circuit Design Based on In-Memory Computin...

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4th international conference on Natural Language Processing, ICNLP 2022

作者： Wu, Jin Wen, Heng Pang, Wenting Chong, Gege Xi'an University of Posts and Telecommunication Dept. School of Electronic Engineering Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

In recent years, with the continuous development and deepening of artificial intelligence, the amount of data required has increased dramatically. With the sharp increase in the amount of data, data access between the memory and the computing unit will cause a lot of energy consumption and increase the read and write latency, that is, the 'memory wall' problem occurs. In order to solve the extra energy loss caused by the 'memory wall' problem in the Von neumann architecture and to achieve better acceleration for neural network algorithms, the in-memory computing architecture is considered a promising processor architecture for future big data applications. In this thesis, we conducted research related to the in-memory computing neural network accelerator architecture based on three kinds of memories as Static Random-Access Memory (SRAM), Resistive Random-Access Memory (ReRAM), and Ferro-electric Field Effect Transistor (FeFET). For non volatile memory, such as ReRAM, FeFET, its non-volatile, low power consumption and other characteristics show obvious advantages. Using VGG-8, VGG-16, and AlexNet networks, the designed in-memory computing architecture is validated on three different datasets. The performance of the designed gas pedal is evaluated in terms of the acceleration effect of each layer and the number of Tile units used in each layer, read/write latency, read/write energy consumption, system occupied area, energy efficiency ratio, and other indicators, the experimental results show that the neural network accelerator based on in-memory calculation designed in this thesis has reached a relatively advanced level. © 2022 IEEE.

关键词： Energy efficiency

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Study on Improved YOLOv7-based Algorithm for Detecting Crack Defects in Ancient Buildings 24

Study on Improved YOLOv7-based Algorithm for Detecting Crack...

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3rd international conference on Computer, Artificial intelligence and Control Engineering, CAICE 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P Xie, Zhiying Yang, Aihua He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment Dongting Lake Area Changsha Hunan410004 China

ISBN: (纸本)9798400716942

In the protection and restoration work of ancient buildings, the detection and analysis of the cracks and defects of the exterior walls of ancient buildings is a crucial task. In this paper, an improved YOLOv7 algorithm is proposed to detect the defects of ancient external walls, aiming to achieve efficient and accurate detection of the defects of ancient external walls. The method involves preprocessing the image using a rectangle clipping algorithm, graying the image using the maximum method, and denoising the image with a median filter. Next, the image is labeled using the LabelImg tool, and the data is split into training, validation, and test sets. During model training, a CBAM-Net module is inserted after several CBS modules in the backbone network, the GIOU loss function is replaced by CIOU, and feature task extraction is performed. The realized attention mechanism is added to the Yolov7 model. The results show that the mAP value of crack detection after the improved algorithm reaches 81.2%. In contrast to the initial YOLOv7 algorithm, the precision of the model has been boosted to 87.2%, exhibiting a 6.0% enhancement in accuracy and an 85.2% improvement in recall rate. The improved YOLOv7 crack detection algorithm reduces network calculation and improves crack classification and recognition ability. This approach is crucial in safeguarding ancient buildings as it enables varying levels of maintenance depending on the severity of cracks. © 2024 ACM.

关键词： Attention mechanism Deep learning Defect detection Image processing YOLOv7

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data Capitalism, Microsoft's Planetary Computer, and the Biodiversity Informatics Community 17th

Data Capitalism, Microsoft's Planetary Computer, and the Bio...

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17th international conference on Information for a Better World - Shaping the Global Future (iconference)

作者： Lukacz, Przemyslaw Matt New York United States

ISBN: (纸本)9783030969578;9783030969561

Joining the growing literature on the design of information infrastructures for the Anthropocene, this paper explores the big Tech's efforts to support biodiversity informatics community. Drawing on the information infrastructure studies, political economy of science, and critical data studies, the paper aims to shed light on the reception of artificial intelligence and cloud computing on the part of a biodiversity data network. The preliminary interview findings reported here suggest a cautious optimism about the funding of biodiversity informatics, yet likewise underscore the importance of considering political-economy of infrastructures, both among their users and designers.

关键词： Environmental information infrastructures Political economy Biodiversity informatics

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An Education Program for big data Security and Privacy

An Education Program for Big Data Security and Privacy

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IEEE international conference on big data Security on Cloud (bigdataSecurity), High Performance and Smart computing (HPSC) and Intelligent data and Security (IDS)

作者： Bhavani Thuraisingham Kim Nimon Latifur Khan The University of Texas at Dallas The University of Texas at Tyler

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

This paper describes the course that we developed at the University of Texas at Dallas on big data Security and Privacy with funding from the National Science Foundation. The project started in 2017 and was completed in 2023. We have taught the course starting Fall 2020, Fall 2021, Fall 2022 and we are teaching it for the fourth time in Fall 2023. This is an extremely popular course and we limit the enrollment to 80 students. Students have some background in cyber security and big data analytics. This paper provides an overview of the organization of the course, the course content, and learning outcomes and the evaluation carried out. The members of the project include those who have expertise in (i) Cyber Security, (ii) big data Analytics, and (iii) Education Research.

关键词： data privacy Education Organizations Machine learning Learning (artificial intelligence) big data Security

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Construction of Smart Library Service System Based on Internet of Things data Technology

Construction of Smart Library Service System Based on Intern...

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2022 IEEE international conference on Knowledge Engineering and Communication Systems, ICKES 2022

作者： Gao, Yuan JiLin Agricultural Science and Technology University Jilin Department of Library Jilin China

ISBN: (纸本)9781665456371

The construction of a smart library is an inevitable trend in the era with the emergence and rapid development of the Internet of Things, AioT, big data, cloud computing, mobile Internet, 5G, artificial intelligence, blockchain, and quantum information technology. Librarians, readers, space, literature resources, and reading promotion activities can all be connected to each other through the Internet of Things data technology after building a smart library, and monitor and evaluate the development process. This article not only introduces the face recognition system, book management system, book recommendation and purchase platform, book positioning system, book self-checkout system, reader service reservation system, reader activity management system and librarian attendance system in smart libraries, and also took the innovative and entrepreneurial talent training service carried out by the library of JiLin Agricultural Science and Technology University as an example, expounding how the smart library operates based on the Internet of Things data technology. © 2022 IEEE.

关键词： Libraries

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Text and Facial Features Recognition using Machine intelligence

Text and Facial Features Recognition using Machine Intellige...

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2022 international conference on Machine Learning, big data, Cloud and Parallel computing, COM-IT-CON 2022

作者： Gopal, Krishna Prasad, Sanjeev Kumar Kumari, Anju Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

We all live in a world and we all are dependent on mobiles especially android for our work. In the era of mobile development, android developers need to build the latest android applications and be in pace with the quickly developing mobile world and to meet the business needs. For meeting worldwide business needs, android applications need to include machine learning methods to make them more effective. To incorporate machine learning techniques into android applications requires a great deal of examination, exploration, and mastery. There are different methodologies for implementing machine learning into versatile applications. One of the proficient ways is to develop an application programming interface (API) utilizing firebase which gives adaptability to the designers to import machine learning models. Google firebase gives a lot more inbuilt tensorflow lite, machine learning models that can be added utilizing the given dependencies. Thus, here we present a methodology for developing an application that extracts text from images and recognizes facial features from photos by utilizing firebase ML Kit and TESTLAB. We can save the written data on images at one place while extracting text with the help of our application. The application shows the facial features of human face on clicking photo or just by uploading any photo. It shows the coordinates of eyes, nose, mouth, eyebrows, recognizes the face shape and provide expression of the face whether the person is happy, sad, angry, laughing etc all with the help of ML *** is a mobile Software Development Kit (SDK) that brings Google's AI ability to android and iOS applications in an amazing way and simple to utilize library. Regardless of whether you are new or experienced in AI, you can execute the activities you want in a clear and simple way. Then again, assuming you are an accomplished machine learning engineer, ML Kit gives advantageous APIs that assist you with utilizing your custom tensorflow lite models i

关键词： Software design

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Towards An Integrated Study of Human Cognition in IT at Deep AI Era 9th

Towards An Integrated Study of Human Cognition in IT at Deep...

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9th international conference on Information Technology and Quantitative Management, ITQM 2022

作者： Chen, Zhengxin College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Deep AI and big data have reshaped the IT field. Since AI refers to the simulation of human intelligence processes by computer systems for problem solving, the current landscape of IT provides an excellent opportunity for us to re-examine how human cognition has responded to new developments in IT. In this paper, we discuss several issues related to human cognition in IT at AI-enriched big data era. First, we note that deep AI reflects human beings' needs for causality and explanations, and review a couple of related aspects. We then examine human cognition in IT from two perspectives: On the theoretical side, recent developments in quantum computing has the potential to reveal complex nature of human cognition in the form of quantum cognition, and on the practical side, cognitive bias may play a negative role, as demonstrated in the way of dealing with fake news. The complexity of human cognition in IT leads to a proposed multi-layer concept description which is based on our view that a complete concept definition is impossible without incorporating human cognitive feedback in the loop. We conclude this paper by emphasizing the need for integrated study of human cognition in IT. © 2022 The Authors. Published by Elsevier B.V.

关键词： big data

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Optimal Revenue Analysis of the Stubborn Mining Based on Markov Decision Process 4th

Optimal Revenue Analysis of the Stubborn Mining Based on Mar...

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4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhang, Yiting Liu, Ming Guo, Jianan Wang, Zhaojie Wang, Yilei Liang, Tiancai Singh, Sunil Kumar School of Computer Science Qufu Normal University Rizhao China China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China CCET Panjab University Chandigarh India

ISBN: (纸本)9783031200984

As one of the most popular cryptocurrencies, Bitcoin is essentially a decentralized ledger. Each node maintains the security of the blockchain through the workload proof mechanism, and the block that obtains the accounting right will receive a block reward in the form of Bitcoin. Because the Bitcoin system follows the "longest legal chain" principle, when a fork occurs, orphan blocks will inevitably be generated, and some miners’ computing power will be a waste. In recent years, researchers have discovered that miners can obtain profits disproportionate to their own computing power by deviating from Bitcoin’s honest mining. Selfish Mining (SM1) is a case in dishonest mining strategy, and dishonest miners (attackers) can obtain higher returns by retaining the blocks they create and selectively delaying their release. The stubborn mining strategy is a generalized form of selfish mining. It increases the revenue of the stubborn miner by adopting a wider range of parameters. Its three mining strategies are: Lead-Stubborn, Equal Fork stubborn and Trail stubborn. The mining problem can be formulated as a Markov Decision Process (MDP), which can be resolved to give the optimal mining strategy. This work describes the three mining strategies of stubborn mining as a Markov decision process, solves it and gives the lower bound of the highest return under the optimal stubborn mining strategy. Our experimental results demonstrate that the revenue of the optimal stubborn mining strategy is higher than SM1 under certain circumstances, and this strategy allows dishonest miners (stubborn miners) to obtain revenue that does not match the actual computing power paid. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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