On the basis of making clear diversity characteristics of soil functions and multiple characteristics of income, this paper points out that the monetization of soil functions based functional maintenance and change de...On the basis of making clear diversity characteristics of soil functions and multiple characteristics of income, this paper points out that the monetization of soil functions based functional maintenance and change decision process can be regarded as a game process of different utilization methods at the background of different functions. The balance of this game process will determine monetary value of soil functions. After understanding money and monetization concepts, it introduces that measurability and exchangeability of soil functions provide objective conditions for monetization of soil functions. Finally, it discusses that usefulness value of soil functions provide basis for monetization of soil functions.展开更多
Enterprise＇s post monetization management is one of the important achievements of business management innovation. The paper defines the meaning of enterprise＇s post monetization management mechanism, which includes ...Enterprise＇s post monetization management is one of the important achievements of business management innovation. The paper defines the meaning of enterprise＇s post monetization management mechanism, which includes benefit, supply, demand, competition and incentive mechanism, and specifically explains the meaning and function of each subsystem in order to profoundly crystallize and grasp the importance of enterprise＇s post monetization management in the aspect of raising business management level.展开更多
Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and ...Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare researchers.To secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.展开更多
Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and c...Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and content distortion caused by inadequate stylization.To address these problems,PhotoGAN,a new Generative AdversarialNetwork(GAN)model is proposed in this paper.A deeper feature extraction network has been designed to capture global information and local details better.Introducingmulti-scale attention modules helps the generator focus on important feature areas at different scales,further enhancing the effectiveness of feature extraction.Using a semantic discriminator helps the generator learn quickly and better understand image content,improving the consistency and visual quality of the generated images.Finally,qualitative and quantitative experiments were conducted on a self-built dataset.The experimental results indicate that PhotoGAN outperformed the current state-of-the-art techniques.It not only performed excellently on objective metrics but also appeared more visually appealing,particularly excelling in handling complex scenes and details.展开更多
基金Supported by Hebei Provincial Social Science Foundation Project in 2012(HB12YJ055)
文摘On the basis of making clear diversity characteristics of soil functions and multiple characteristics of income, this paper points out that the monetization of soil functions based functional maintenance and change decision process can be regarded as a game process of different utilization methods at the background of different functions. The balance of this game process will determine monetary value of soil functions. After understanding money and monetization concepts, it introduces that measurability and exchangeability of soil functions provide objective conditions for monetization of soil functions. Finally, it discusses that usefulness value of soil functions provide basis for monetization of soil functions.
文摘Enterprise＇s post monetization management is one of the important achievements of business management innovation. The paper defines the meaning of enterprise＇s post monetization management mechanism, which includes benefit, supply, demand, competition and incentive mechanism, and specifically explains the meaning and function of each subsystem in order to profoundly crystallize and grasp the importance of enterprise＇s post monetization management in the aspect of raising business management level.
文摘Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare researchers.To secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.
基金funded by the Key R&D and Transformation Projects of Xizang(Tibet)Autonomous Region Science and Technology Program(funder:the Department of Science and Technology of the Xizang(Tibet)Autonomous Region),funding(grant)number:XZ202401ZY0004.
文摘Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and content distortion caused by inadequate stylization.To address these problems,PhotoGAN,a new Generative AdversarialNetwork(GAN)model is proposed in this paper.A deeper feature extraction network has been designed to capture global information and local details better.Introducingmulti-scale attention modules helps the generator focus on important feature areas at different scales,further enhancing the effectiveness of feature extraction.Using a semantic discriminator helps the generator learn quickly and better understand image content,improving the consistency and visual quality of the generated images.Finally,qualitative and quantitative experiments were conducted on a self-built dataset.The experimental results indicate that PhotoGAN outperformed the current state-of-the-art techniques.It not only performed excellently on objective metrics but also appeared more visually appealing,particularly excelling in handling complex scenes and details.
