With the rapid development of information technology, digitalization has gradually integrated into the daily lives of modern people, becoming an indispensable and important component. Especially, DingTalk software, as...
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The existing intelligent transportation system for device management and control may have some challenges, such as simplistic traffic device management, difficult traffic datastorage and management, and difficult to ...
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Augmented Reality (AR) technology has lately received considerable attention for its packages in diverse domains which includes gaming, education, and productivity tools. However, notwithstanding advances in AR input ...
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With the development and widespread application of Model-Based Systems Engineering (MBSE) in the aerospace domain continue to grow, System Modeling Language (SysML) has become increasingly important as the most popula...
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The Machine-Learning-as-a-Service (MLaaS) framework allows one to grab low-hanging fruit of machine learning techniques and data science, without either much expertise for this sophisticated sphere or provision of spe...
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ISBN:
(纸本)1577358872
The Machine-Learning-as-a-Service (MLaaS) framework allows one to grab low-hanging fruit of machine learning techniques and data science, without either much expertise for this sophisticated sphere or provision of specific infrastructures. However, the requirement of revealing all training data to the service provider raises new concerns in terms of privacy leakage, storage consumption, efficiency, bandwidth, etc. In this paper, we propose a lightweight privacy-preserving MLaaS framework by combining Compressive Sensing (CS) and Generative Networks. It's constructed on the favorable facts observed in recent works that general inference tasks could be fulfilled with generative networks and classifier trained on compressed measurements, since the generator could model the data distribution and capture discriminative information which are useful for classification. To improve the performance of the MLaaS framework, the supervised generative models of the server are trained and optimized with prior knowledge provided by the client. In order to prevent the service provider from recovering the original data as well as identifying the queried results, a noise-addition mechanism is designed and adopted into the compressed data domain. Empirical results confirmed its performance superiority in accuracy and resource consumption against state-of-the-art privacy-preserving MLaaS frameworks.
Texts, printed or written, are the most important form of cultural preservation. This is true for Chinese studies and many cultures. For this reason, optical character recognition (OCR) is one of the most important to...
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This paper introduces a data transmission and processing system for the speed governor test-bed. And the design employed the Internet of things (IOT) technology, added the WIFI communication module on the traditional ...
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This article establishes a power grid physical asset analysis and rating system based on big datatechnology. This system achieves the acquisition, storage, analysis, and presentation of massive data through the compr...
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The cold chain logistics of oysters starts from the moment they are harvested and ends when they reach the consumers' homes. The process includes various stages: pre-processing, packaging, pre-cooling, processing,...
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In this paper, the engineering of pulse power supply in electromagnetic launch technology is studied in depth, the advantages and disadvantages of optical fiber communication transmission currently used are analyzed, ...
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