The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Inst...The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.展开更多
Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management....Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management. Tool development in this regard will help researchers quickly identify variety information. This study photographed apricot fruits outdoors and indoors and constructed a dataset that can precisely classify the fruits using a U-net model (F-score:99%), which helps to obtain the fruit's size, shape, and color features. Meanwhile, a variety search engine was constructed, which can search and identify variety from the database according to the above features. Besides, a mobile and web application (ApricotView) was developed, and the construction mode can be also applied to other varieties of fruit trees.Additionally, we have collected four difficult-to-identify seed datasets and used the VGG16 model for training, with an accuracy of 97%, which provided an important basis for ApricotView. To address the difficulties in data collection bottlenecking apricot phenomics research, we developed the first apricot database platform of its kind (ApricotDIAP, http://apricotdiap.com/) to accumulate, manage, and publicize scientific data of apricot.展开更多
要拥有一次教育信息化的机遇,那么移动互联网和自带设备则是一次重要的机缘,它不仅可以激起学生们向往学习的动力,还能激起正处于一线教学的教师们对于应用的热情。利用教育云平台辅助的M-Lear ni ng教学模式,能让学生真正成为学习的主...要拥有一次教育信息化的机遇,那么移动互联网和自带设备则是一次重要的机缘,它不仅可以激起学生们向往学习的动力,还能激起正处于一线教学的教师们对于应用的热情。利用教育云平台辅助的M-Lear ni ng教学模式,能让学生真正成为学习的主体。本文探讨该模式下的数字媒体技术专业教学改革效果,从学生学习态度、学习效果等方面收集数据并进行数据分析,通过贝叶斯网络模型,对学生的学情进行分析和预测,为下一步的教学提供决策支持。展开更多
In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the trainin...In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.展开更多
Conventional analysis methods cannot fully meet the business needs of power grids.At present,several artificial intelligence (AI) projects in a single business field are competing with each other,and the interfaces be...Conventional analysis methods cannot fully meet the business needs of power grids.At present,several artificial intelligence (AI) projects in a single business field are competing with each other,and the interfaces between the systems lack unified specifications.Therefore,it is imperative to establish a comprehensive service platform.In this paper,an AI platform framework for power fields is proposed;it adopts the deep learning technology to support natural language processing and computer vision services.On one hand,it can provide an algorithm,a model,and service support for power-enterprise applications,and on the other hand,it can provide a large number of heterogeneous data processing,algorithm libraries,intelligent services,model managements,typical application scenarios,and other services for different levels of business personnel.The establishment of the platform framework could break data barrier,improve portability of technology,avoid the investment waste caused by repeated constructions,and lay the foundation for the construction of "platform + application + service" ecological chain.展开更多
基金sponsored by the National Natural Science Foundation of P.R.China(Nos.62102194 and 62102196)Six Talent Peaks Project of Jiangsu Province(No.RJFW-111)Postgraduate Research and Practice Innovation Program of Jiangsu Province(Nos.KYCX23_1087 and KYCX22_1027).
文摘The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.
基金supported by the Fundamental Research Funds for the Central Non-profit Research Institution of the Chinese Academy of Forestry (Grant No.CAFYBB2020ZY003)the Key S&T Project of Inner Mongolia (Grant No.2021ZD0041-001-002)the Central Public-interest Scientific Institution Basal Research Fund (Grant No.11024316000202300001)。
文摘Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management. Tool development in this regard will help researchers quickly identify variety information. This study photographed apricot fruits outdoors and indoors and constructed a dataset that can precisely classify the fruits using a U-net model (F-score:99%), which helps to obtain the fruit's size, shape, and color features. Meanwhile, a variety search engine was constructed, which can search and identify variety from the database according to the above features. Besides, a mobile and web application (ApricotView) was developed, and the construction mode can be also applied to other varieties of fruit trees.Additionally, we have collected four difficult-to-identify seed datasets and used the VGG16 model for training, with an accuracy of 97%, which provided an important basis for ApricotView. To address the difficulties in data collection bottlenecking apricot phenomics research, we developed the first apricot database platform of its kind (ApricotDIAP, http://apricotdiap.com/) to accumulate, manage, and publicize scientific data of apricot.
文摘要拥有一次教育信息化的机遇,那么移动互联网和自带设备则是一次重要的机缘,它不仅可以激起学生们向往学习的动力,还能激起正处于一线教学的教师们对于应用的热情。利用教育云平台辅助的M-Lear ni ng教学模式,能让学生真正成为学习的主体。本文探讨该模式下的数字媒体技术专业教学改革效果,从学生学习态度、学习效果等方面收集数据并进行数据分析,通过贝叶斯网络模型,对学生的学情进行分析和预测,为下一步的教学提供决策支持。
基金National Key Research and Development Program of China(2021ZD0113704).
文摘In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.
文摘现有的光流估计网络为了获得更高的精度,往往使用相关性成本量和门控循环单元(gate recurrent unit,GRU)来进行迭代优化,但是这样会导致计算量大并限制了在边缘设备上的部署性能。为了实现更轻量的光流估计方法,本文提出局部约束与局部扩张模块(local constraint and local dilation module,LC-LD module),通过结合卷积和一次轴注意力来替代自注意力,以较低的计算量对每个匹配特征点周边区域内不同重要程度的关注,生成更准确的相关性成本量,进而降低迭代次数,达到更轻量化的目的。其次,提出了混洗凸优化上采样,通过将分组卷积、混洗操作与凸优化上采样相结合,在实现其参数数量降低的同时进一步提高精度。实验结果证明了该方法在保证高精度的同时,运行效率显著提升,具有较高的应用前景。
基金supported by National Key Research and Development Project(2017YFE0112600)Science and Technology Project of China Electric Power Research Institute(Research on the Key Technologies and Typical Application Scenarios of the Artificial Intelligence Basic Framework for Integrated Energy)
文摘Conventional analysis methods cannot fully meet the business needs of power grids.At present,several artificial intelligence (AI) projects in a single business field are competing with each other,and the interfaces between the systems lack unified specifications.Therefore,it is imperative to establish a comprehensive service platform.In this paper,an AI platform framework for power fields is proposed;it adopts the deep learning technology to support natural language processing and computer vision services.On one hand,it can provide an algorithm,a model,and service support for power-enterprise applications,and on the other hand,it can provide a large number of heterogeneous data processing,algorithm libraries,intelligent services,model managements,typical application scenarios,and other services for different levels of business personnel.The establishment of the platform framework could break data barrier,improve portability of technology,avoid the investment waste caused by repeated constructions,and lay the foundation for the construction of "platform + application + service" ecological chain.
