Wireless body area networks (WBANs), a relatively new technology that has emerged in response to the exponential growth in the demand for healthcare, have shown themselves to be promising and are already being utilize...
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Autonomous computing is the key concept for service orchestration in next-generation cloud computing environments. Virtualization supplements it by adding an abstraction layer to the services, as well as to the underl...
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Efficient resource allocation among slices/users with different Service Level Agreements (SLAs) is a critical task in 5G+ networks, which has prompted recent research into Deep Neural networks (DNNs). However, challen...
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The proceedings contain 95 papers. The topics discussed include: distributed sparse random projection trees for constructing K-nearest neighbor graphs;fast deterministic gathering with detection on arbitrary graphs: t...
ISBN:
(纸本)9798350337662
The proceedings contain 95 papers. The topics discussed include: distributed sparse random projection trees for constructing K-nearest neighbor graphs;fast deterministic gathering with detection on arbitrary graphs: the power of many robots;accurate and efficient distributed covid-19 spread prediction based on a large-scale time-varying people mobility graph;accelerating packet processing in container overlay networks via packet-level parallelism;efficient hardware primitives for immediate memory reclamation in optimistic data structures;efficient hardware primitives for immediate memory reclamation in optimistic data structures;accelerating distributed deep learning training with compression assisted Allgather and reduce-scatter communication;accelerating CNN inference on long vector architectures via co-design;exploiting input tensor dynamics in activation checkpointing for efficient training on GPU;drill: log-based anomaly detection for large-scale storage systems using source code analysis;dynasparse: accelerating GNN inference through dynamic sparsity exploitation;exploiting sparsity in pruned neural networks to optimize large model training;SRC: mitigate I/O throughput degradation in network congestion control of disaggregated storage systems;boosting multi-block repair in cloud storage systems with wide-stripe erasure coding;on doorway egress by autonomous robots;and on the arithmetic intensity of distributed-memory dense matrix multiplication involving a symmetric input matrix (SYMM).
The comet assay is a versatile method used to determine the DNA damage in individual cells. The cells processed by this technique preserve the stable genetic material in the head of the comet and the unstable portion ...
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ISBN:
(纸本)9783031829307;9783031829314
The comet assay is a versatile method used to determine the DNA damage in individual cells. The cells processed by this technique preserve the stable genetic material in the head of the comet and the unstable portion of DNA in the tail. The analysis of the resulting microscopic images from this test must be carried out by an expert, who must precisely determine the extent of DNA liberated from the head of the comet, since it is related to the level of damage. Since this is a time-consuming and very specialized task, the objective of this research is to develop a computational system, based on the use of a convolutional neural network, for the automatic classification of cells processed by comet assay according to the level of DNA damage they present.
Intensive Care Units usually carry patients with a serious risk of mortality. Recent research has shown the ability of Machine Learning to indicate the patients' mortality risk and point physicians toward individu...
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ISBN:
(纸本)9798350312249
Intensive Care Units usually carry patients with a serious risk of mortality. Recent research has shown the ability of Machine Learning to indicate the patients' mortality risk and point physicians toward individuals with a heightened need for care. Nevertheless, healthcare data is often subject to privacy regulations and can therefore not be easily shared in order to build Centralized Machine Learning models that use the combined data of multiple hospitals. Federated Learning is a Machine Learning framework designed for data privacy that can be used to circumvent this problem. In this study, we evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage. We compare the predictive performance of Federated, Centralized, and Local Machine Learning in terms of AUPRC, F1-score, and AUROC. Our results show that Federated Learning performs equally well as the centralized approach (for 2, 4, and 8 clients) and is substantially better than the local approach, thus providing a viable solution for early Intensive Care Unit mortality prediction. In addition, we demonstrate that the prediction performance is higher when the patient history window is closer to discharge or death. Finally, we show that using the F1-score as an early stopping metric can stabilize and increase the performance of our approach for the task at hand.
Emergency response systems, water treatment facilities, wastewater collection systems, Oil and gas pipelines, electrical power transmission systems, wind farms, defence networks, and large-scale communication networks...
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distributed denial-of-service (DDoS) attacks remain one of the major security threats in the Internet of Things (IoT) domain. Compared to traditional computing devices, IoT devices typically have more limited computat...
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Recently, the link prediction methods based on enclosing subgraph extraction and line graph transformation have been proven to achieve excellent prediction accuracy, but there are still some shortcomings, for examples...
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Facing the computational challenges in mobile devices within blockchain networks, particularly the scarcity and underutilization of computational resources, this paper introduces the CAGE Framework: a novel architectu...
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Facing the computational challenges in mobile devices within blockchain networks, particularly the scarcity and underutilization of computational resources, this paper introduces the CAGE Framework: a novel architecture based on cooperative game theory within alliance blockchains. Designed to optimize computational resource allocation across multiple mobile terminals, CAGE Framework leverages a tri-layer structure - comprising the Blockchain Network Layer, User Network Layer, and distributed Collaborative Computing Layer - to facilitate efficient resource sharing and task scheduling. Through intelligent contracts, the framework automatically aggregates user demands, utilizing the InterPlanetary File System (IPFS) for data storage, thereby enhancing privacy protection and blockchain data throughput. Validated on the Hyperledger Fabric platform and benchmarked against state-of-the-art approaches, CAGE demonstrates superior transaction throughput, reduced latency, and enhanced resource efficiency. The core strategy, dubbed CAGE, is predicated on cooperative gaming, aiming to maximize user satisfaction by balancing energy consumption, computational load, and resource allocation multi-objectively. Experiments reveal a notable improvement in system load balancing (by 51%) and a significant reduction in energy consumption (by 62%), affirming the framework's efficacy in addressing computational resource deficiencies both within and outside the alliance under low energy and balanced load conditions. The CAGE Framework not only charts anew path for computational resource optimization in mobile blockchain networks but also lays a theoretical and practical foundation for the furtherance of blockchain technology application and optimization.
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