We propose and experiment a Social Networking Service (SNS) for local communities for tsunami disaster control. It is an easy-to-use GIS-based system with powerful GIS analysis capabilities. One of the features of the...
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ISBN:
(纸本)9780780397408
We propose and experiment a Social Networking Service (SNS) for local communities for tsunami disaster control. It is an easy-to-use GIS-based system with powerful GIS analysis capabilities. One of the features of the system is resident participation. The GIS layer structure proposed in this paper nicely supports this user participation. The system architecture and the use of the system for tsunami disaster control are discussed. We report ongoing developments in Hachinohe-City, Japan.
The computation of ripple effect is based on the effect that a change to a single variable will have on the rest of a program, it determines the scope of the change and provides a measure of the program's complexi...
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ISBN:
(纸本)0889864640
The computation of ripple effect is based on the effect that a change to a single variable will have on the rest of a program, it determines the scope of the change and provides a measure of the program's complexity. Previous research has shown that measurement of ripple effect can give us valuable information about procedural software. This paper explores the applicability of ripple effect to measuring object oriented code, particularly C++. Extensions are proposed to the computation of the ripple effect to accommodate aspects of the object oriented paradigm.
Quality of Service (QoS) management is critical for service-oriented enterprise architectures because services have different QoS characteristics, requesters have different requirements, and service interactions are d...
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Quality of Service (QoS) management is critical for service-oriented enterprise architectures because services have different QoS characteristics, requesters have different requirements, and service interactions are decoupled. This paper proposes a QoS Management Architecture for dynamic processing of service- and flow-level quality attributes to support QoS requests and analyses in Web-service-oriented architectures. The architecture is implemented using business Process Execution Language for Web Services (BPEL4WS), an interoperable integration model that facilitates automated process integration). The proposed approach extends BPEL4WS by integrating it with Web service-level agreements (WSLA) to support QoS and extending the BPEL4WS language to provide a new " " assertion that describes the location of a document's WSLA document. Under the proposed approach, quality attributes are defined, computed, and acted upon as dynamic characteristics of systems, with values constantly changing in operation The feasibility of the proposed approach is demonstrated using an illustrative travel reservation service flow example.
Object-Oriented Information System (OOIS) is an information system that employs object-oriented technologies in system design and implementation. Recent research advances and industrial innovations in distributed syst...
Object-Oriented Information System (OOIS) is an information system that employs object-oriented technologies in system design and implementation. Recent research advances and industrial innovations in distributed system modeling and Internet applications have enabled OOIS design and implementation to be carried out on the basis of new technologies and platforms. This special section on Modeling Object-Oriented Information Systems presents readers with a set of best papers selected from the 7th International Conference on OOIS. Reviews of theories and applications of OOIS’s are also provided for predicating trends in OOIS modeling.
The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount ...
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The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to new prospects for deep learning. However, this data is confined within data silos and contains sensitive information, making it difficult to be processed in a centralized manner, particularly under stringent data privacy regulations. Federated learning (FL) offers a solution by enabling collaborative learning while ensuring privacy. Nonetheless, data and device heterogeneity complicate FL implementation. This research presents a specialized FL algorithm for heterogeneous edge computing. It integrates a lightweight grouping strategy for homogeneous devices, a scheduling algorithm within groups, and a Split Learning (SL) approach. These contributions enhance model accuracy and training speed, alleviate the burden on resource-constrained devices, and strengthen privacy. Experimental results demonstrate that the GSFL outperforms FedAvg and SplitFed by 6.53× and 1.18×. Under experimental conditions with \(\alpha=0.05\), representing a highly heterogeneous data distribution typical of extreme Non-IID scenarios, GSFL showed better accuracy compared to FedAvg by 10.64%, HACCS by 4.53%, and Cluster-HSFL by 1.16%. GSFL effectively balances privacy protection and computational efficiency for real-world applications in mobile multimedia communications.
This book presents the latest trends in scientific methods and enabling technologies to advance e-business. It consists of selected high-quality papers from the 16th International Conference on E-businessengineering ...
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ISBN:
(数字)9783030349868
ISBN:
(纸本)9783030349851
This book presents the latest trends in scientific methods and enabling technologies to advance e-business. It consists of selected high-quality papers from the 16th International Conference on E-businessengineering (ICEBE 2019), held in Shanghai, China, on 11–13 October 2019. ICEBE is a leading international forum for researchers, engineers, and business specialists to exchange cutting-edge ideas, findings, and experiences in the field of e-business. The book covers a range of topics, including agents for e-business, big data for e-business, Internet of Things, mobile and autonomous computing, security/privacy/trust, service-oriented and cloud computing, softwareengineering, blockchain, and industry applications.
As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile,...
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As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).
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