The orchestration of a service function chain (SFC) is responsible for the coordination of multiple virtual network functions (VNFs) in a sequence and the deployment of VNF nodes and links onto physical resources, for...
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Periodic B-spline (PBS) has been a successfully used technique in histopathology image segmentation. However, it is limited when dealing with multi-instance objects in real-world biomedical applications. Moreover, its...
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This paper studies an energy-efficient task scheduling problem that takes into account the cooperation among service caching-enabled mobile edge computing (MEC) servers. We consider a MEC system consisting of multiple...
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Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics appl...
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Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics applications in *** feeding previous power electronic data into the learning model,accurate information is drawn,and the quality of IoT-based power services is ***,the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow *** efficient execution of this data-intensive Power Workflow(PW)needs massive computing resources,which are available in the cloud ***,the execution efficiency of PW decreases due to inappropriate sub-task and data *** addition,the power consumption explodes due to massive data *** address these challenges,a PW placement method named PWP is ***,the Non-dominated Sorting Differential Evolution(NSDE)is used to generate placement *** simulation experiments show that PWP achieves the best trade-off among data acquisition time,power consumption,load distribution and privacy preservation,confirming that PWP is effective for the placement problem.
Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solve...
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Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the *** this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for *** main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data ***,we conducted extensive experiments to evaluate the UAV-ITD *** results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.
An in-memory storage system provides submillisecond latency and improves the concurrency of user applications by caching data into memory from external storage. Fault tolerance of in-memory storage systems is essentia...
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An in-memory storage system provides submillisecond latency and improves the concurrency of user applications by caching data into memory from external storage. Fault tolerance of in-memory storage systems is essential, as the loss of cached data requires access to data from external storage, which evidently increases the response latency. Typically, replication and erasure code (EC) are two fault-tolerant schemes that pose different trade-offs between access performance and storage usage. To help make the best performance and space trade-off, we design ElasticMem, a hybrid fault-tolerant distributed in-memory storage system that supports elastic redundancy transition to dynamically change the fault-tolerant scheme. ElasticMem exploits a novel EC-oriented replication (EOR) that carefully designs the data placement of replication according to the future data layout of EC to enhance the I/O efficiency of redundancy transition. ElasticMem solves the consistency problem caused by concurrent data accesses via a lightweight table-based scheme combined with data bypassing. It detects correlated read and write requests and serves subsequent read requests with local data. We implement a prototype that realizes ElasticMem based on Memcached. Experiments show that ElasticMem remarkably reduces the time of redundancy transition, the overall latency of correlated concurrent data accesses, and the latency of single data access among them.
Device-to-device (D2D)-enabled mobile edge computing (MEC) is a promising communication network in which computation tasks on mobile devices (MDs) can not only be processed locally but also can be scheduled to the MEC...
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Label distribution learning is a powerful learning paradigm to deal with label polysemy and has been widely applied in many practical tasks. A significant obstacle to the effective utilization of label distribution is...
Model updates are exchanged between server(s) and participants in Federated Learning (FL), which can result in excessive delay, especially for large models. Existing communication-efficient FL approaches such as quant...
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Model updates are exchanged between server(s) and participants in Federated Learning (FL), which can result in excessive delay, especially for large models. Existing communication-efficient FL approaches such as quantization and top-k sampling apply compression to gradients assuming that gradients are sparse and can tolerate small deviations. This can hardly be applied to down-link transmission. In this work, we employ compressive sensing on model parameters instead of gradients and propose a two-way adaptive compression scheme, Cepe-FL, which exploits dictionary learning to project non-sparse model parameters into sparse representations to ensure reconstruction accuracy. Cepe-FL supports joint model reconstruction with drastic reduction in computational complexity from $O(n)$ to $O(1)$. Cepe-FL adjusts the compression ratio adaptively according to the training loss, achieving the best trade-off between communication and model precision. Furthermore, it demonstrates efficacy in defending against membership inference attacks since only compressed models are exchanged. We conduct extensive experiments on three image classification tasks and compare with three communication-efficient approaches including FedPAQ, FedAvg and T-FedAvg. Cepe-FL presents the best performance in all tasks under IID and non-IID scenarios. We also implement white-box membership inference attacks, and the results show Cepe-FL can significantly suppress success ratio of inference in comparison with other approaches. IEEE
Data sparsity poses a significant challenge for recommendation systems, prompting the research of Cross-Domain Recommendation (CDR). CDR aims to leverage more user-item interaction information from source domains to i...
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