The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by...
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Cloud service providers are currently storing large volumes of data on cloud. This requires verification mechanisms often employing cryptography by third party auditors. However, ensuring the security and privacy of e...
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Root cause analysis is crucial for cloud-native systems. However, existing supervised approaches ignore the potential of unlabeled data, which is frequent in the cloud-native root cause analysis scenarios. Moreover, t...
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ISBN:
(纸本)9798350344868;9798350344851
Root cause analysis is crucial for cloud-native systems. However, existing supervised approaches ignore the potential of unlabeled data, which is frequent in the cloud-native root cause analysis scenarios. Moreover, the class-imbalanced distribution of faults presents obstacles to applying semi-supervised learning. To overcome these limitations, we propose STRCA, a metrics-based semi-supervised self-training approach for root cause analysis. Furthermore, STRCA employs minority priority self-training, which selects pseudo-labels of high quality during generations. Additionally, the stepwise distribution alignment is introduced to rebalance the predicted distribution with gradually decreasing strength. These two strategies mitigate the class-imbalance of data in semi-supervised learning. Experiments on the public dataset show the effectiveness of STRCA with limited labels.
Accurate and efficient traffic flow velocity prediction is very important for intelligent transportation system. But traffic flow velocity prediction faces the following challenges: the data is dynamic in time and spa...
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Intelligent transportation systems (ITS) use the latest technologies for real-time traffic control and monitoring to ensure efficient traffic management and reduce the risks of traffic accidents. The microscopic level...
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ISBN:
(纸本)9798350369458;9798350369441
Intelligent transportation systems (ITS) use the latest technologies for real-time traffic control and monitoring to ensure efficient traffic management and reduce the risks of traffic accidents. The microscopic level of traffic modeling is the most appropriate level for controlling and monitoring the interaction between vehicles based on car-following scenarios. However, the data retrieved from sensor networks can be affected by measurement errors, and consequently the implementation of appropriate mechanisms to overcome their propagation to the control system is mandatory. This paper aims to analyse the current research in the calibration of car-following models and provide valuable insights of recent developments in this field. To achieve this goal, VOSviewer has been chosen as a visualisation tool to create bibliographic maps based on the output from the well-known scientific database Clarivate Analytics Web of science (WoS). The maps obtained provide a visual representation of the main institutions involved in this field of research and identify the research interests based on author and indexing keywords. Furthermore, this paper analyses the top five clusters identified based on the analysis of co-occurrence keywords, presenting discussions about the connections existing within these clusters.
Effective data management across large-scale shared-use materials-science facility centers is crucial, as high-end facilities available at such centers are indispensable for cutting-edge research in modern materials s...
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ISBN:
(纸本)9798400702341
Effective data management across large-scale shared-use materials-science facility centers is crucial, as high-end facilities available at such centers are indispensable for cutting-edge research in modern materials science. However, establishing network connectivity for these facilities is often not practically feasible due to its uniqueness, forcing users to resort to the traditional manual method of data acquisition and transfer, such as the use of DVD disks. In this paper, we introduce an IoT device designed to facilitate direct data transfer from these non-networked and shared-use facilities to a cloud system. We propose a novel data transfer algorithm that integrates user management with data transfer processes for safely assigning each user's data to its private storage space on the cloud. The proposed technology serves as a fundamental building block in shaping a smarter laboratory information management system for the advancement of material researches and developments.
Vehicular Ad-hoc Networks (VANETs) serve as an integral component within Intelligent Transportation systems (ITS), designed to augment traffic fluidity and ensure a secure, comfortable driving environment for both dri...
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IT infrastructure contains many different systems and devices, in which the uncertainty and non-uniformity of data sources is an urgent problem to be solved. Therefore, this paper proposes the research of Agent-based ...
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These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling...
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ISBN:
(纸本)9798350307887
These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identities patterns and temporal influences with low execution lime, showcasing its potential in financial system incident prediction.
Surveillance cameras, even with Pan, Tilt, and Zoom (PTZ) capabilities, can only cover a limited directional range at a time, leading to inevitable blind spots. To mitigate these blind spots, users typically need to d...
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