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检索条件"任意字段=9th IEEE International Enterprise Distributed Object Computing Conference"

共 1225 条记录，以下是61-70 订阅

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Kubitect - a Solution for On-premise Cluster Deployment 15

Kubitect - a Solution for On-premise Cluster Deployment

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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： Music, Din Fortuna, Carolina Jozef Stefan Inst Dept Commun Syst SI-1000 Ljubljana Slovenia Jozef Stefan Int Postgrad Sch Jamova 39 SI-1000 Ljubljana Slovenia

ISBN: (纸本)9781665460873

Deploying compute clusters is easy and user-friendly when using modern public cloud solutions, however alternative open source solutions for smaller, non-enterprise setups, such as hobby projects, home labs, or small and micro enterprises are currently missing. In this paper, we identify challenges for on-premise environments and propose Kubitect - a solution that allows anyone to set up their own on-premises private cloud that can run containerized applications and is easy to maintain. Unlike the existing state of the art, the proposed solution enables the creation of clusters on multiple physical hosts using a single declarative configuration file that can be easily understood and versioned. the paper describes the user and automated workflows of Kubitect, elaborates on the design choices for implementation and provides a qualitative and quantitative evaluation. the high-availability of on on-premises cluster deployment is also validated.

关键词： cloud computing non-enterprise on-premise cluster lightweight orchestration

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Illicit item detection in X-ray images for security applications 9

Illicit item detection in X-ray images for security applicat...

引用

9th ieee international conference on Big Data computing Service and Applications, BigDataService 2023

作者： Batsis, Georgios Mademlis, Ioannis Papadopoulos, Georgios th. Harokopio University of Athens Department of Informatics and Telematics Athens Greece

ISBN: (纸本)9798350333794

Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. the large volume and high throughput of passengers, mailed parcels, etc., during rush hours make it a Big Data analysis task. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage, anchor-based object detectors. this paper proposes a two-fold improvement of such algorithms for the X-ray analysis domain, introducing two complementary novelties. Firstly, more efficient anchors are obtained by hierarchical clustering the sizes of the ground-truth training set bounding boxes;thus, the resulting anchors follow a natural hierarchy aligned with the semantic structure of the data. Secondly, the default Non-Maximum Suppression (NMS) algorithm at the end of the object detection pipeline is modified to better handle occluded object detection and to reduce the number of false predictions, by inserting the Efficient Intersection over Union (E-IoU) metric into the Weighted Cluster NMS method. E-IoU provides more discriminative geometrical correlations between the candidate bounding boxes/Regions-of-Interest (RoIs). the proposed method is implemented on a common single-stage object detector (YOLOv5) and its experimental evaluation on a relevant public dataset indicates significant accuracy gains over both the baseline and competing approaches. this highlights the potential of Big Data analysis in enhancing public safety. © 2023 ieee.

关键词： Deep neural networks

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Context is King: an enterprise Model that Connects the Internal Structure with the Business Environment 25

Context is King: an Enterprise Model that Connects the Inter...

引用

25th ieee international enterprise distributed object computing conference (ieee EDOC)

作者： Bider, Ilia Perjons, Erik Bork, Dominik Stockholm Univ Stockholm Sweden Univ Tartu Tartu Estonia TU Wien Business Informat Grp Vienna Austria

ISBN: (纸本)9781665444880

If an enterprise Model is to be used in the strategic decision-making, it should represent the connections between the elements of internal structure, like processes, machines, people, and the elements of the business environment, like market segments, competitors, regulators. the paper presents and discusses a modeling technique - Fractal enterprise Model (FEM) - that allows to represent such connections, and a computerized toolkit - FEM toolkit - that supports the modeling process. the presentation is done based on a running example. the attendees will learn about an innovative presentation of a business environment in an enterprise model.

关键词： enterprise model environment modeling tool FEM

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Efficient Online Edge Learning for UAV object Detection via Adaptive Batch Size Fitting 9

Efficient Online Edge Learning for UAV Object Detection via ...

引用

9th international conference on Big Data computing and Communications, BigCom 2023

作者： Wu, Feiyu Qu, Yuben Wang, Weijun Dong, Chao Jing, Yuqian Zhang, Lei Wu, Qihui Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology China Nanjing University China

ISBN: (纸本)9798350331240

Benefiting from flexibility and mobility, Unmanned Aerial Vehicle (UAV) plays an important role in object detection. However, it is challenging to maintain high accurate inference results in an inherent dynamic scene. Because data drift caused by changeable weather, motion blur, etc leads to inference deviation from the pre-training model. Online learning is an effective method for data drift by supplementing actual environmental data. However, recent researches about online learning are unsuitable for resource-constrained and computationally complex UAV object detection, because of excessive training time for online learning. To improve recognition accuracy and shorten training time, this paper designs an efficient online edge learning for UAV object detection via adaptive batch size fitting. It comprehensively considers online samples and the model's real-time performance to fit the batch size adaptively. Our method efficiently improves the convergence speed of model training and can be easily implemented on the embedded devices of UAVs. Experimental results show that compared with the existing batch size fitting methods, our method reduces the total training time by 30% and has strong robustness in different scenes. © 2023 ieee.

关键词： object detection

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Towards Small Scale Urban Fire Detection Dataset 9

Towards Small Scale Urban Fire Detection Dataset

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9th ieee international conference on Cyber Security and Cloud computing and 8th ieee international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Dai, Sida Han, Tao Yang, Mingchuan Zhang, Yuan Wang, Lichuan China Telecom Research Institute College of Emerging Technology Beijing China Zhejiang University College of Information Science and Electronic Engineering China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

A novel dataset with the goal of advancing the state-of-the-art in fire detection by focusing the question of fire detection in the context of small-scale urban fire detection is proposed in this paper. this is achieved by gathering images of various scenes containing the small or early fire in urban areas. Flames are labeled using bounding boxes through manual work to aid in object localization. Up till now, there are over a total of 2600 images labeled in our dataset. the creation of the proposed dataset draws people's attention to the detection performance of common fire detecting algorithms on such small fire datasets. the experimental results show the comparison between the proposed dataset and other fire detection datasets. the baseline performance analysis provided by several popular detection models for the bounding box detection results is also presented in the experiment of the paper. © 2022 ieee.

关键词： object detection

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Toward a Maturity Model for Digital Business Ecosystems from an IT perspective 25

Toward a Maturity Model for Digital Business Ecosystems from...

引用

25th ieee international enterprise distributed object computing conference (ieee EDOC)

作者： Ehrensperger, Robert Sauerwein, Clemens Breu, Ruth Univ Innsbruck Inst Comp Sci A-6020 Innsbruck Austria

ISBN: (纸本)9781665435796

the digital transformation affects longstanding business models and leads to competition not only between enterprises but also between supply chains and networks. the concept of digital business ecosystems (DBEs) allows enterprises to concentrate on network co-creation and co-evolution of bundled services and products across enterprise boundaries. this study investigates the status of existing DBEs and their maturity. We interviewed 28 experts, reviewed more than 7,108 publications, and derived a maturity model from the obtained results. this maturity model helps practitioners to identify opportunities for improvement in their collaboration within DBEs and help researchers to assess and compare the current maturity level of existing DBEs.

关键词： Digital business ecosystems Maturity models enterprise architecture management Expert interviews

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Proceedings - 2023 ieee international conference on Smart computing, SMARTCOMP 2023

Proceedings - 2023 IEEE International Conference on Smart Co...

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9th ieee international conference on Smart computing, SMARTCOMP 2023

ISBN: (纸本)9798350322811

the proceedings contain 71 papers. the topics discussed include: addressing APC data sparsity in predicting occupancy and delay of transit buses: a multitask learning approach;combining public human activity recognition datasets to mitigate labeled data scarcity;a systematic study on object recognition using millimeter-wave radar;vision transformer-based real-time camouflaged object detection system at edge;elixir: a system to enhance data quality for multiple analytics on a video stream;detecting potholes from dashboard camera images using ensemble of classification mechanisms;an online continuous semantic segmentation framework with minimal labeling efforts;cooperative multi-agent reinforcement learning for large scale variable speed limit control;a novel weight dropout approach to accelerate the neural network controller embedded implementation on FPGA for a solar inverter;on learning data-driven models for in-flight drone battery discharge estimation from real data;a demo of microservice for customized faulty product detection system in smart manufacturing;and real-world community-in-the-loop smart video surveillance system.

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Towards Ontology-based Competence Modeling in enterprise Architecture 25

Towards Ontology-based Competence Modeling in Enterprise Arc...

引用

25th ieee international enterprise distributed object computing conference (ieee EDOC)

作者： Calhau, Rodrigo F. Azevedo, Carlos L. B. Almeida, Joao Paulo A. Fed Univ Espirito Santo UFES Ontol & Conceptual Modeling Res Grp NEMO Vitoria ES Brazil Fed Inst Espirito Santo IFES LEDS Serra Brazil

ISBN: (纸本)9781665435796

Organizations today can hardly thrive without continuous attention to human resources development. In this context, competence-based approaches have been receiving increasing attention, as the availability of qualified people with the right combination of competences establishes itself as a major success factor. this paper investigates the integration of competence-based approaches into enterprise Architecture modeling. We follow a principled approach to propose competence modeling representation strategies in enterprise Architecture. We first identify a key set of competence-related concepts, analyze them based on the Unified Foundational Ontology, and then propose well-founded representation patterns for competence modeling in the ArchiMate language. We discuss how these patterns can be embedded in competence-based practices for the enterprise.

关键词： Competences Competence Modeling enterprise Architecture

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Automatic Tooth Enumeration on Panoramic Radiographs Using Deep Learning 9

Automatic Tooth Enumeration on Panoramic Radiographs Using D...

引用

9th ieee international conference on computing, Engineering and Design, ICCED 2023

作者： Fariza, Arna Asmara, Rengga Rojaby, Muhammad Oktavian Fajar Astuti, Eha Renwi Putra, Ramadhan Hardani Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Airlangga Department of Dentistry Surabaya Indonesia

ISBN: (纸本)9798350370126

Accurate tooth numbering is essential for dental procedures, treatment planning, and patient record management. Automatic tooth enumeration in dental panoramic radiographs is crucial in modern dental image analysis and diagnosis. Tooth enumeration in panoramic radiographs has relied on handcrafted features and traditional image processing techniques. Still, these methods often lack the accuracy and robustness required for complex cases and varied image qualities. Despite the advancements in deep learning-based object detection algorithms, a significant research gap remains in the specific domain of automatic tooth enumeration on panoramic radiographs. this research paper presents an innovative approach for automatic tooth enumeration on panoramic radiographs using the state-of-the-art You Only Look Once (YOLO) object detection framework focused on implementing the YOLOv5 library. the YOLOv5 model is evaluated as an efficient system capable of accurately detecting and enumerating individual teeth from panoramic radiographs. the 612 panoramic radiograph images evaluation shows the bounding-box results show the best tooth detection rate in the YOLOv5x model. the YOLOv5 model is generally very good at predicting tooth enumeration on panoramic radiographs in a relatively small dataset. this advancement is expected to enhance dental diagnosis and treatment planning, benefiting dental professionals and patients. © 2023 ieee.

关键词： object detection

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A Framework for Scalable object Storage and Retrieval Considering Privacy Concerns: A Case Study on the Signature Detection 9

A Framework for Scalable Object Storage and Retrieval Consid...

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9th international conference on Web Research, ICWR 2023

作者： Khomami, Mohammad Mehdi Daliri Saghiri, Ali Mohammad Rezvanian, Alireza Meybodi, Mohammad Reza Computer Engineering Amirkabir University of Technology Soft Computing Laboratory Department Tehran Iran University of Science and Culture Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350399691

Nowadays, we are faced with a huge amount of private data generated in different ecosystems, including the Internet of things, social networks, peer-to-peer networks, and e-commerce, to mention a few. the performance of traditional object storage and retrieval systems concerning response time is going to be degraded by a drastic increase in generated data. this problem becomes more challenging when a specific private object should be found. To address the mentioned problem, this paper suggests scalable object storage and retrieval. To do this, three well-known methods, content-addressable networks from peer-to-peer systems, vector space model from information retrieval systems, and siamese neural networks from neural networks theory, are involved in a framework to bring a scalable system to solve the mentioned problems. the suggested framework is unique to the best of our knowledge because there is no fusion among mentioned fields reported so far. A case study on signature detection is also conducted to evaluate the proposed framework. the results show that, compared to a centralized system, the proposed framework significantly decreases the response time for detecting a signature while maintaining the same accuracy. In addition, the proposed mechanism does not require the private objects to be sent to a central entity, which helps to alleviate privacy concerns. © 2023 ieee.

关键词： Peer to peer networks

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