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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Deep learning (DL) has attracted interest in healthcare for disease diagnosis systems in medical imaging analysis (MedIA) and is especially applicable in Big data environments like federated learning (FL) and edge com...
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Since China's accession to the WTO, import and export trade has developed rapidly. With the development of information technology construction, enterprises have accumulated a large amount of business data. This ar...
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ISBN:
(纸本)9798350388350;9798350388343
Since China's accession to the WTO, import and export trade has developed rapidly. With the development of information technology construction, enterprises have accumulated a large amount of business data. This article discusses how to use this data for fast and accurate support decision-making. On the basis of research on data warehouse technology and decision support systems, a decision support system for import and export trade based on data warehouse technology is proposed and implemented. The content includes system architecture, data warehouse model, online analytical processing strategy, and data mining technology. The key technologies of the system are also elaborated. This system realizes the conversion of data held by enterprises into information, and then into knowledge, in order to improve the decision-making ability, efficiency, and accuracy of enterprises. The experiment shows that the system can effectively process and analyze trade data, mine useful information from massive data, and reliably and efficiently store and analyze trade data, helping enterprises formulate marketing plans.
The accuracy of short-term load forecasting has improved with the development of deep learning on complete datasets. However, load data is often lost due to various complex factors in the power system, causing it is a...
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ISBN:
(纸本)9798350359329;9798350359312
The accuracy of short-term load forecasting has improved with the development of deep learning on complete datasets. However, load data is often lost due to various complex factors in the power system, causing it is a challenge for incomplete load forecasting. To address this issue, this paper proposes an end-to-end load-completion and forecasting network. Specially, we designed the Wavelet-enhanced Generative Adversarial Imputation Net (waveGAIN) to improve the accuracy of data completion. Then, we propose an end-to-end training strategy that combines waveGAIN and Bidirectional Long Short-Term Memory (BiLSTM) networks to improve the accuracy of data filling and reduce the load forecasting error. The experimental results show that our proposed method improves the prediction accuracy in the case of different patterns and levels of missing data. And our method achieves good results even in case of 80% missing data.
This paper proposes a method for generating the surface model of hollow turbine blades based on the fusion of Industrial Computerized Tomography (ICT) multi-directional slice data. Through the Normal Distribution Tran...
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Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model faces poor generalization issue due t...
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ISBN:
(纸本)9798350377712;9798350377705
Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model faces poor generalization issue due to inter-city domainshift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization, but suffers from slow convergence rate because of vehicles' surrounding heterogeneity across cities. Going beyond existing HFL works that have deficient capabilities in complex tasks, we propose a rapid-converged heterogeneous HFL framework (FedRC) to address the inter-city data heterogeneity and accelerate HFL model convergence rate. In our proposed FedRC framework, both single RGB image and RGB dataset are modelled as Gaussian distributions in HFL aggregation weight design. This approach not only differentiates each RGB sample instead of typically equalizing them, but also considers both data volume and statistical properties rather than simply taking data quantity into consideration. Extensive experiments on the TriSU task using across-city datasets demonstrate that FedRC converges faster than the state-of-the-art benchmark by 38.7%, 37.5%, 35.5%, and 40.6% in terms of mIoU, mPrecision, mRecall, and mF1, respectively. Furthermore, qualitative evaluations in the CARLA simulation environment confirm that the proposed FedRC framework delivers top-tier performance.
The exponential growth of medical data presents a significant challenge for modern healthcare systems: how to conduct research and meet other needs while safeguarding patient privacy. This paper proposes a data privac...
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Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods...
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ISBN:
(纸本)9798350307887
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to generate high -quality fake user profiles to achieve 1) transferability among black-box RSs 2) and imperceptibility among detectors. In order to achieve these goals, we introduce textual reviews of products to enhance the generation quality of the profiles. Specifically, we propose a novel attack framework named R-Trojan, which formulates the attack objectives as an optimization problem and adopts a tailored transformer-based generative adversarial network (GAN) to solve it so that highquality attack profiles can be produced. Comprehensive experiments on real-world datasets demonstrate that R-Trojan greatly outperforms state-of-the-art attack methods on various victim RSs under black-box settings and show its good imperceptibility.
In this study, we explore a new strategy aimed at enhancing emergency response operations by integrating various data sources, such as IoT devices, drones, crowdsourced information, and real-time weather data. The uni...
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Medical data analysis is crucial for advancing healthcare but is often hindered by the scarcity and high cost of medical data. This paper presents a novel approach to improve neural network performance in medical data...
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