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Application of distributed Back Propagation Neural Network for Dynamic Real-Time Bidding 1

引用

11th international Advanced computing conference, IACC 2021

作者： Desai, Ankit thakkar, Hiren Kumar Singh, Priyanka Bhargavi, Lakshmi Sai Bangalore 560068 India Department of Computer Engineering Marwadi University Gujarat Rajkot360003 India Department of Computer Science and Engineering SRM University Andhra Pradesh Amaravati522240 India

ISBN: (数字)9783030955021

ISBN: (纸本)9783030955014

Programmatic buying popularly known as real-time bidding (RTB) is a key ascendancy in online advertising. the Ad Tech industry is experiencing sustained growth, especially due to the increased use of mobile devices. While data has become essential for targeting and ad performance, data businesses have become difficult to differentiate due to their proliferation, as well as limitations of attribution. this provides an opportunity for Big Data practitioners to leverage this data and use machine learning to improve efficiency and make more profits. Taking such an opportunity we came up with an application of a machine learning algorithm, distributed back propagation neural network (d-bpnn) to predict bid prices in a real-time bidding system. this paper depicts how d-bpnn is used to achieve less eCPM (effective Cost Per Mille) for advertisers while preserving win rate and budget utilization. © 2022, Springer Nature Switzerland AG.

关键词： Budget control

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A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a distributed Environment 11th

A Lightweight FCNN-Driven Approach to Concrete Composition E...

引用

11th European-Alliance-for-Innovation (EAI) international conference on Cloud computing (CloudComp)

作者： Lu, Hui Kamoto, Kondwani Michael Liu, Qi Zhang, Yiming Liu, Xiaodong Xu, Xiaolong Qi, Lianyong Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Sch Comp & Software Minist Educ Nanjing 210044 Peoples R China Hebei Univ Technol Sch Civil & Transportat Engn Tianjin 300401 Peoples R China Edinburgh Napier Univ Sch Comp Edinburgh EH14 1DJ Midlothian Scotland

ISBN: (纸本)9783030991913;9783030991906

It is of great significance to study the positive characteristics of concrete bearing cracks, fire and other adverse environment for the safety of human life and property and the protection of environmental resources. However, there are still some challenges in traditional concrete composition evaluation methods. On the one hand, the traditional method needs a lot of experimental work, which is time-consuming and laborious;On the other hand, the cost of new technology is high, and its applicability needs further study. therefore, this paper proposes an improved lightweight model based on fully connected neural network (FCNN) to discover the relationship between the performance of different concrete mixtures and the visual (image) performance of the final synthesis process, so as to realize the prediction of concrete composition. the model is built in a distributed environment, and it can achieve lightweight and convenient effect through remote call learning model. the experimental results show that the method greatly improves the accuracy of concrete composition prediction.

关键词： Concrete Lightweight FCNN distributed

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Data Exploration Optimization for Medical Big Data 11th

Data Exploration Optimization for Medical Big Data

引用

11th international conference on Health Information Science, HIS 2022

作者： Ding, Shuang Mao, Chenyu Zheng, Wenkui Xiao, Qi Wu, Yitao School of Software Henan University Kaifeng475004 China Henan International Joint Laboratory of Intelligent Network Theory and Key Technology Kaifeng475004 China Henan University Kaifeng475004 China International College Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783031206269

In medical domain, huge amounts of data are generated at all times. Data exploration is very important to help physicians or medical researchers to find the required datasets. However, the storage and computation of large-scale data surpass the performance limits of traditional relational databases, which are prone to performance bottlenecks, and it is difficult to expand the storage capacity and computational power. To solve the above problems, this paper studies the distributed migration storage and computing methods of medical data, and proposes a distributed computing and storage strategy of medical data to achieve efficient medical data exploration. We use the open source tools to migrate the MIMIC-IV medical database and optimize the sepsis data exploration. the experimental results show that compared with the traditional single node method, our optimization method based on Hive-ORC and its indexes, partition table reduces the storage space by 85% and the query time by 86%. this mechanism has higher efficiency in data management in the medical field. In addition, with the increase of data volume and cluster nodes, the strategy can achieve better optimization results. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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On Enhancing Network Slicing Life-Cycle through an AI-Native Orchestration Architecture 1

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37th international conference on Advanced Information networking and Applications, AINA 2023

作者： Moreira, Rodrigo Martins, Joberto S. B. Carvalho, Tereza C. M. B. Silva, Flávio de Oliveira Institute of Exact and Technological Sciences Federal University of Viçosa Rio Paranaíba38810-000 Brazil UNIFACS Salvador41820-020 Brazil USP São Paulo05508-010 Brazil Faculty of Computing Federal University of Uberlândia Uberlândia38400-902 Brazil

ISBN: (数字)9783031284519

ISBN: (纸本)9783031284502

Legacy experimental network infrastructures can still host innovative services through novel network slicing orchestration architectures. Network slicing orchestration architectures available in state-of-the-art have building blocks that structurally change depending on the problem they are trying to solve. In these orchestrators, life-cycle functions of network slices experience advances on numerous fronts, such as combinatorial methods and Artificial Intelligence (AI). However, many of the state-of-the-art slicing architectures are not AI-native, making heterogeneity and the coexistence and use of machine learning paradigms for network slicing orchestration hard. Also, using AI in a non-native way makes network slice management a challenger and shallow. Hence, this paper proposes and evaluates a distributed AI-native slicing orchestration architecture that delivers machine learning capabilities in all life cycles of a network slice. Carried experiments suggest lower error using distributed machine learning models to predict Radio Access Network (RAN) resource consumption in slicing deployed over different target domains. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Life cycle

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Heralding the Future of Federated Learning Framework: Architecture, Tools and Future Directions 11

Heralding the Future of Federated Learning Framework: Archit...

引用

11th international conference on Cloud computing, Data Science and Engineering (Confluence)

作者： Das, Saneev Kumar Bebortta, Sujit Coll Engn & Technol Dept Comp Sci & Engn Bhubaneswar India

ISBN: (纸本)9781665414517

In today's era, the exponential growth of data and its management is a matter of concern. Machine learning has shown its efficacy in multiple application areas. But machine learning on decentralized data was a hectic task since last decade. A novel technology has gained much importance in recent days i.e., federated learning which deals with training on decentralized and distributed data along with preservation of its privacy. Smartphone data being privacy-sensitive is used for locally training a global model which further is aggregated to generate an updated global model which again is distributed among multiple clients. this paper focuses on presenting the efficacy of federated learning by epitomizing an architecture showing the working mechanism of the technology. Further, this paper exhibits an intersection of on-device machine learning, privacy preservation technology and edge computing i.e., federated learning. Also, we have used TensorFlow Federated, an open source platform to simulate federated learning tasks for MNIST and extended MNIST (E-MNIST) datasets. Further, the results contain the loss and accuracy parameters for ten iterations repeated for six optimizer states (Opt(st)) for each dataset. the peak accuracy that we achieved for MNIST and E-MNIST datasets are 0.843 and 0.853 respectively by using federated averaging algorithm. Further, the minimum loss value that we obtained for MNIST and E-MNIST datasets are 0.652 and 0.646 respectively. the execution time for implementing the algorithm for each dataset is presented in a graphical manner. Finally, certain application areas where federated learning technology has aided are scrutinized.

关键词： Federated Learning Federated Averaging MNIST E-MNIST TensorFlow Federated (TFF) Edge computing

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An Overview of Smart Contracts for Non-Programmers

An Overview of Smart Contracts for Non-Programmers

引用

Electrical, Electronic and computing Engineering (IcETRAN), international conference on

作者： Helena Anišić Dinu Dragan Dušan B. Gajić Veljko B. Petrović Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

the evolution and widespread adoption of distributed Ledger Technology (DLT) across various sectors underscore its potential to transform conventional processes and systems. Among the innovations spurred by DLT, smart contracts emerge as a pivotal application, automating contractual obligations. However, the development of smart contracts poses significant challenges, particularly for non-programmers, due to the necessity for specialised programming skills and understanding of blockchain technology. this complexity restricts broader user engagement and risks the incorporation of errors with potentially severe consequences. To address this challenge, researchers have proposed various approaches to enable non-programmers to create smart contracts in a user-friendly way. through this study, we seek to answer pivotal research questions regarding the spectrum of available development approaches for non-programmers and the methods employed to assess their usability. Our findings indicate a notable scarcity of user-centered evaluations, underscoring the need for more robust and comparative usability testing in this domain. By illuminating the current landscape and identifying areas for further research and development, this paper endeavors to contribute to the inclusive expansion of smart contract technology, ensuring its accessibility and utility for a broader user base.

关键词： Technological innovation Codes distributed ledger Smart contracts Transforms Programming Writing

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A distributed Intrusion Detection System based on Blockchain and Federated Learning

A Distributed Intrusion Detection System based on Blockchain...

引用

IEEE international conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Dagula Lei Xu Keke Gai Liehuang Zhu School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

In the face of rapidly evolving network attacks and the large volume of network traffic data, distributed intrusion detection has garnered significant research interest. However, distributed intrusion detection requires multiple parties to share data, which may pose a challenge of sensitive data leakage. this paper presents IDS-BF, a distributed intrusion detection system based on blockchain and federated learning. To improve the effectiveness of federated learning, the proposed system adopts a contribution-based method to dynamically adjust the weights of clients when aggregating the local gradients. Specially, to ensure the fairness of contribution evaluation, the proposed system utilizes two parachains to perform model aggregation and contribution evaluation respectively. With the help of a relay chain, the two parachains can communicate with each other. Simulation results on real-world data show that the proposed aggregation strategy can help to improve the accuracy of global model, achieving an increase of up to 8% compared to traditional strategies.

关键词： Accuracy Federated learning Simulation Intrusion detection Information sharing Telecommunication traffic Data models Blockchains Security Relays

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Neural Network Based Intelligent Traffic System 11

Neural Network Based Intelligent Traffic System

引用

11th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems - Technology and Applications (IDAACS)

作者： Saathvik, B. M. Gupta, Vinayak Kedia, Ayush Asawa, Lov Subramanian, Karpagavalli PES Univ Dept Elect & Commun Engn Bangalore 560085 Karnataka India

ISBN: (纸本)9781665426053

As years pass by, population and vehicular mobility in most of the cities are rapidly growing which leads to a lot of traffic congestion at junctions. this also makes lots of emergency vehicles like Ambulance, Firefighters, etc to stand in traffic snarls which leads to loss of life during the delay of these vehicles. this paper outlines the development of the Intelligent Traffic System (ITS) prototype by providing solutions to these problems which can handle traffic congestion at right time without manual intervention. ITS uses a neural network model and Internet of things (IoT) unit to calculate the number of vehicles and emergency vehicles in the traffic lanes and then control the traffic signal based on it. A neural model algorithm is designed in such a way that the model is capable of detecting the ambulance from the live video as well as still images. Eventually, the IoT unit will provide the density of the vehicles in each lane and accordingly allocate the timing for signal transition in the following sequence: RED, YELLOW, GREEN, and the cycle repeats.

关键词： Neural Network IoT Raspberry Pi Computer networking

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Edge Task Offloading Method for Power Internet of things Based on Multi-round Combined Auction 11th

Edge Task Offloading Method for Power Internet of Things Bas...

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11th international conference on Computer Engineering and Networks, CENet2021

作者： Ge, Yi Wang, Ying Cheng, Yufan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811665530

As a key technology of power Internet of things, multi-access edge (MEC) computing promotes the deep integration of Internet of things (IoT) and smart grid. However, most of the current studies carry out task offloading and resource allocation from the perspective of benefit to the user side, ignoring the motivation of the edge side to provide services. In this paper, a multi-round combined auction algorithm is proposed for the power Internet of things scenario of multi-terminal business-multi-edge IoT agent, which can meet the needs of different levels of business while maximizing the edge side profits. the experimental results show that this method can effectively improve the edge profit, and has a good effect on the success rate of task offloading and the resource utilization of the edge IoT agent. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Resource allocation

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Research and Implementation of Publish/Subscribe Communication Model Based on OPC UA 11

Research and Implementation of Publish/Subscribe Communicati...

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11th international conference of Information and Communication Technology, ICTech 2022

作者： Chai, Anying Ma, Yue Yin, Zhenyu He, Zhiyun Bi, Zhiying Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware Software Shenyang110168 China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

In recent years, intelligent manufacturing has developed rapidly. Industrial IoT technologies are applied to different industrial scenarios. OPC UA is a service-oriented architecture, which has good interoperability. At the same time, it is a cross-platform communication standard that can realize the interconnection between heterogeneous network devices and solve the problem of information silos in the industrial IoT field. However, with the gradual increase of industrial equipment, the amount of data transmission in the network is increasing. the traditional OPC UA communication method based on client/server mode has defects such as tight coupling and performance bottleneck. It cannot meet the high throughput demand of the network. therefore, these phenomena lead to longer time delays and lower transmission efficiency of network communication systems. To solve the above problems, this paper proposes a publish/subscribe communication model based on OPC UA. We design the overall architecture of the OPC UA publish/subscribe model and adopt a message agent mechanism to realize distributed communication of OPC UA. this model has functions such as message modeling, address space construction, and publish/subscribe. the architecture of this communication model is compatible with the C/S model, which can ensure compatibility and coexistence with the traditional OPC UA communication system. Meanwhile, in order to improve the distinguished service capability of the OPC UA system, a multi-priority data scheduling algorithm is proposed and integrated into the publish/subscribe communication model to improve the efficiency of real-time data transmission in industrial networks. the experimental results show that the communication model can accomplish distributed communication in industrial networks and be better applied in wireless sensor networks. the scheduling algorithm included in the model significantly improves the efficiency of real-time data transmission and reduces the

关键词： Heterogeneous networks

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