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检索条件"任意字段=8th IEEE International Enterprise Distributed Object Computing Conference"

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Structure of resources in an enterprise IT organization subject to optimal defect detection 10

Structure of resources in an enterprise IT organization subj...

引用

10th international Scientific conference on Computer Science, COMSCI 2022

作者： Nakov, Plamen Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9781665497770

At a decade of distributed cloud, hyper-automation, digitalization, Internet of things (IoT) world, use of large volume IT resources in business is inevitable. Organizations use vast amount of IT systems to automate processes and tend to focus on digitalization at all costs. Automation and day-to-day business require huge computing power at a high-availability and reliability. Cybersecurity is crucial for success of every business. To address these needs, organizations must have in-depth understanding of their own resource structure (assets, network, services, applications, relationships, etc.). this paper focuses on how organizations can keep information about their resources and analyzes how organizations can ensure optimal defect detection in its own resource *** paper proposes unique approach for structuring of multi-layer resources, focused on formalization of detection, localization, and effective defect remediation. © 2022 ieee.

关键词： Defects

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object Detection Algorithm Based on Channel-Spatial Fusion Cascade Attention

Object Detection Algorithm Based on Channel-Spatial Fusion C...

引用

ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Guojun Mao Guanyi Liao Hengliang Zhu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Fujian University of Technology Fuzhou China

ISBN: (纸本)9781665456579

To solve the problem of low accuracy and high leakage rate of small object detection in a one-stage object detection model, a cascade attention mechanism (CAtt) is proposed. Cascading attention integrates channel attention and spital attention mechanisms that can be flexibly embedded into any object detection model, such as YoloV4, to improve the performance of the object detection model. the attention mechanism is perfected from two aspects: one is to integrate channel attention and spatial attention respectively so that the two are more complementary. Secondly, by introducing the skipping connection mechanism, the shallow detail information is connected with the deep semantic information, to solve the problem of information loss caused by the multiple convolutions of the model. Four typical attention mechanisms were selected to compare public datasets such as PASCAL VOC2007 and PASCAL VOC2012. A large number of experimental results show that this method significantly improves the performance of the object detection model, especially for the PASCAL VOC2007. Embedding the method in YoloV4 resulted in a 4.77% increase in the mAP index of the original model.

关键词： Cloud computing Semantics object detection Intelligent systems

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Signboard Detection using YOLOv5 with Transfer Learning

Signboard Detection using YOLOv5 with Transfer Learning

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international conference for Convergence of Technology (I2CT)

作者： Uday Kulkarni Meena S M Akshay Shanbhag Amruta A Ayachit Prajakta Kulkarni Shivani Halemani Supreeth S Kadappanavar KLE Technological University Hubli Karnataka

With the ever increasing popularity of autonomous driving technology, effective and precise detection and identification of traffic signs is critical for subsequent decision-making and control actions. the huge elevation in technology has made it more feasible to classify and detect images. the precision and inference time are the key factors for object detection in real-time. the introduction of high-performance computing resources, cutting-edge deep learning, and machine learning algorithms offer real-time solutions to the above issues. After a detailed survey and study that exhibited different methods to address these issue, we have proposed a deep learning model which is based on one of the recent versions of YOLO. In this paper, we have proposed YOLOv5 with transfer learning as an optimum solution to address the mentioned issue. the proposed method has various potential parameters that turn out better than the other models of deep learning including the older versions of YOLO. the dataset is taken from ’roboflow’ platform consisting of 19 different classes of signboards, each of 1272 images. the proposed and implemented model gives overall high performance and detection accuracy. this model gives 96% accuracy and 0.93 mAP. the model can be equipped with ADAS for better navigation and safety.

关键词： Deep learning Surveys Machine learning algorithms Navigation High performance computing Transfer learning Neural networks

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TOSCA4QC: Two Modeling Styles for TOSCA to Automate the Deployment and Orchestration of Quantum Applications 24

TOSCA4QC: Two Modeling Styles for TOSCA to Automate the Depl...

引用

24th ieee international enterprise distributed object computing conference (ieee EDOC)

作者： Wild, Karoline Breitenbucher, Uwe Harzenetter, Lukas Leymann, Frank Vietz, Daniel Zimmermann, Michael Univ Stuttgart Inst Architecture Applicat Syst Univ Str 38 D-70569 Stuttgart Germany

ISBN: (纸本)9781728164731

Quantum computing introduces a new computing paradigm that promises to solve problems that cannot be solved by classical computers efficiently. thus, quantum applications will be more and more integrated in classical applications. To bring these composite applications into production, technologies for an automated deployment and orchestration are required to avoid manual error-prone and time-consuming processes. For non-quantum applications, a variety of deployment technologies have been developed in recent years. However, the deployment of quantum applications currently differs significantly from non-quantum applications and thus, leads to a different modeling procedure for the deployment of quantum applications. To overcome these problems, we propose TOSCA4QC that introduces two deployment modeling styles based on the Topology and Orchestration Specification for Cloud Applications (TOSCA) standard for automating the deployment and orchestration of quantum applications: (i) SDK-specific modeling style to cover all technical deployment details and (ii) SDK-agnostic modeling style supporting common modeling principles. We further show how existing model-driven development (MDD) approach can be applied to refine a SDK-agnostic model to an executable SDK-specific model. We demonstrate the practical feasibility by a prototypical implementation as an extension of the TOSCA ecosystem OpenTOSCA and three case studies with IBMQ and a quantum simulator.

关键词： TOSCA Quantum computing Deployment Automation Modeling Orchestration

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Domestic Violence Crisis Recognition Method based on Bi-LSTM+Attention 8

Domestic Violence Crisis Recognition Method based on Bi-LSTM...

引用

8th Annual international conference on Network and Information Systems for Computers, ICNISC 2022

作者： Wang, Zhixiao Yan, Wenyao Li, Zhuochun Huang, Min Fan, Qinyuan Wang, Xin Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Network Computing and Security Xi'an China College of Xi'an Innovation Yan'an University Xi'an710100 China University of Pittsburgh PittsburghPA15260 United States Art and Design Xi'an University of Technology Xi'an710048 China Fakultät Verkehrs- und Maschinensysteme Technische Universität Berlin Berlin13353 Germany Xi'an University of Technology Computer Science and Engineering Xi'an710048 China

ISBN: (纸本)9781665453516

Domestic violence (DV) is getting more and more attention due to the critical trouble and danger that pose great challenge to human rights and health. the popularity of various social medias can help these victims to share their experiences and obtain more possible support from numerous open communities. However, it is not only inefficient but also laborious and time-consuming to manually access and browse through a great deal of available posts on the Internet and social media space. therefore, considering the advantages of Deep Learning (DL) technology in Natural Language Processing, adopting DL as an efficient strategy for automatic recognition and evaluation of DV victims is in the urgent requirements. this paper takes social media Facebook posts about domestic violence as the research object and uses Word2Vec to build word vector model. the paper uses Bi-LSTM+self-Attention deep learning models to accomplish DV crisis recognition task and compares it with CNN, RNN and LSTM. the assessment of experimental results show that the accuracy of CNN and LSTM are both above 90% which is better than RNN;And the accuracy of Bi-LSTM is the highest after using the Attention mechanism. the research results of this article will provide reference and help for public DVCS to rescue DV victims. © 2022 ieee.

关键词： Long short-term memory

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Focus on objects: An Image Super-Resolution Model for Region of Interest

Focus on Objects: An Image Super-Resolution Model for Region...

引用

ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Xiaolin Xing Zhuoyuan Li Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665456579

Image super-resolution (SR) can reconstruct a low-resolution (LR) image into a high-resolution (HR) image to provide an excellent visual experience. However, this task is pixel-wise, and for images with an ordinary resolution, its processing speed is inefficient, which is challenging to meet the needs of practical applications. People only focus on local regions in the image and step back to super-resolve these regions of interest, which can significantly reduce the computational complexity. We propose an image SR model that only focuses on the region of interest in one image, effectively uses people’s visual attention and significantly reduces computing power consumption.

关键词： Visualization Power demand Correlation Computational modeling Superresolution object detection Computational complexity

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A Multimodal Fusion Scene Graph Generation Method Based on Semantic Description

A Multimodal Fusion Scene Graph Generation Method Based on S...

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ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Liwen Ma Weifeng Liu Yaning Wang Shaanxi University of Science and Technology Xi’an China Hangzhou Dianzi University School of Artificial Intelligence Hangzhou China

ISBN: (纸本)9781665456579

For the scene graph generation task, a multimodal fusion scene graph generation method based on semantic description is proposed considering the problems of long-tail distribution and low frequency of high-level semantic interactions in the dataset. Firstly, target detection and relationship inference are performed on the image to construct an image scene graph. Second, the semantic descriptions are transformed into semantic graphs, which are fed into a pre-trained scene graph parser to construct semantic scene graphs. Finally, the two scene graphs are aligned for display and the information of nodes and edges are updated to obtain a fused scene graph with more comprehensive coverage and more accurate semantic interaction information.

关键词： Cloud computing Image edge detection Semantics object detection Task analysis Intelligent systems

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Tree species identification method based on improved YOLOv7

Tree species identification method based on improved YOLOv7

引用

ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Boyu Hu Minling Zhu Lei Chen Lei Huang Ping Chen Mingdong He Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665456579

the paper presents a natural tree species recognition methods based on YOLOv7. We propose a new small target detection layer based on the YOLOv7 network, use the improved Mosaic-8, and introduce the attention mechanism. On the basis of not affecting the detection speed of YOLOv7, we improve the detection accuracy. Experiments show that the method has stronger learning ability, and accuracy than other algorithms under the same conditions.

关键词： Training Image recognition Vegetation Forestry object detection Feature extraction Data processing

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Fruit Detection and Recognition Using Faster R-CNN with FPN30 Pre-trained Network

Fruit Detection and Recognition Using Faster R-CNN with FPN3...

引用

Recent Advances and Innovations in Engineering (ICRAIE)

作者： Muhammad Izzat Roslan Zaidah Ibrahim Nur Aina Khadijah Adnan Norizan Mat Diah Nur Azima Alya Narawi Yunifa Miftachul Arif College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia

Accurate and reliable fruit detection and recognition in orchards is critical for enabling higher-level agriculture tasks such as fruit picking. However, detecting and recognizing fruits with occlusion by neighboring fruits is extremely difficult. Faster R-CNN (Faster Region-based Convolutional Neural Network) is a well-known deep learning technology for object detection and recognition. thus, this study investigates the application of Faster R-CNN for apple detection and recognition. Two different datasets have been constructed under variable illumination conditions and occlusion; an inter-class dataset that consists of images of apples and oranges, and an intra-class dataset that consists of images of two types of apples, namely fuji and royal gala apples. Results indicate that Faster R-CNN can detect and recognize apples from oranges, and the fuji apple in the orchards, with high accuracy. this suggests that Faster R-CNN can be used practically in the real orchard context.

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High-precision visual navigation and localization method

High-precision visual navigation and localization method

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ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Zhiyong Peng Dongsheng Li Longtao Cai Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781665456579

the paper studies high-precision visual navigation and localization technology which has avoided the impact of dynamic objects. the dynamic feature points in the scene are eliminated by the dynamic object detection algorithm based on the YOLACT instance segmentation and geometric epipolar constraint, and the algorithm is integrated into SLAM to achieve the improved three-dimensional location of the dynamic scene, based on the TUM RGB-D data set, the localization accuracy and the running speed of algorithm are tested respectively. the new algorithm is compared with traditional PL-SLAM and ORB-SLAM algorithms. the test results show that the new algorithm average running time on the three data set is less 30.229s than the traditional PL-SLAM algorithm. Compared with the traditional PL-SLAM algorithm and ORB-SLAM algorithm, the root mean square error of absolute trajectory is reduced by 0.084m and 0.130m respectively.

关键词： Location awareness Visualization Simultaneous localization and mapping Navigation Heuristic algorithms object detection Feature extraction

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