With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dyn...With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.展开更多
In the cloud computing environment, outsourcing service mode of data storage causes the security problem, the reliability of data cannot be guaranteed, and the privacy preservation problem has aroused wide concern. In...In the cloud computing environment, outsourcing service mode of data storage causes the security problem, the reliability of data cannot be guaranteed, and the privacy preservation problem has aroused wide concern. In order to solve the problem of inefficiency and high-complexity caused by traditional privacy preservation methods such as data encryption and access control technology, a privacy preservation method based on data coloring is proposed. The data coloring model is established and the coloring mechanism is adopted to deal with the sensitive data of numerical attributes, and the cloud model similarity measurement based on arithmetic average least-approximability is adopted to authenticate the ownership of privacy data. On the premise of high availability of data, the method strengthens the security of the privacy information. Then, the performance, validity and the parameter errors of the algorithm are quantitatively analyzed by the experiments using the UCI dataset. Under the same conditions of privacy preservation requirements, the proposed method can track privacy leakage efficiently and reduce privacy leakage risks. Compared with the k-anonymity approach, the proposed method enhances the computational time efficiency by 18.5%.展开更多
为解决因害虫尺度多样性导致其识别度相对较低的问题,本研究提出了一种基于PP-YOLO(PaddlePaddleYou Only Look once)的农业病虫害识别算法。选取2359个病虫害样本数据集,按照9∶1的比例进行训练集、测试集的划分;选择PP-YOLO模型进行...为解决因害虫尺度多样性导致其识别度相对较低的问题,本研究提出了一种基于PP-YOLO(PaddlePaddleYou Only Look once)的农业病虫害识别算法。选取2359个病虫害样本数据集,按照9∶1的比例进行训练集、测试集的划分;选择PP-YOLO模型进行病虫害监测,并利用平均精度mAP(mean average precision)指标进行模型精度评价;探讨PP-YOLO结合数据增强mixup、颜色扭曲法在病虫害中小目标检测上的适用性。结果表明,PP-YOLO模型在病虫害中小目标检测方面mAP达47.4%、26.5%;基于PP-YOLO模型结合数据增强mixup与颜色扭曲后在病虫害中小目标检测上mAP分别提升4.3%、2.9%。总之,PP-YOLO模型可有效检测识别农作物害虫,同时,数据增强mixup与颜色扭曲法可有效提升病虫害的数据样本指标。展开更多
Understanding the ocean＇s role in the global carbon cycle and its response to environmental change requires a high spatio-temporal resolution of observation.Merging ocean color data from multiple sources is an effect...Understanding the ocean＇s role in the global carbon cycle and its response to environmental change requires a high spatio-temporal resolution of observation.Merging ocean color data from multiple sources is an effective way to alleviate the limitation of individual ocean color sensors（e.g.,swath width and gaps,cloudy or rainy weather,and sun glint） and to improve the temporal and spatial coverage.Since the missions of Sea-Viewing Wide Field-of-View Sensor（Sea Wi FS） and Medium-spectral Resolution Imaging Spectrometer（MERIS） ended on December 11,2010 and May 9,2012,respectively,the number of available ocean color sensors has declined,reducing the benefits of the merged ocean color data with respect to the spatial and temporal coverage.In present work,Medium Resolution Spectral Imager（MERSI）/FY-3 of China is added in merged processing and a new dataset of global ocean chlorophyll a（Chl a） concentration（2000–2015） is generated from the remote sensing reflectance（Rrs（λ）） observations of MERIS,Moderate-resolution imaging spectra-radiometer（MODIS）-AQUA,Visible infrared Imaging Radiometer（VIIRS） and MERSI.These data resources are first merged into unified remote sensing reflectance data,and then Chl a concentration data are inversed using the combined Chl a algorithm of color index-based algorithm（CIA） and OC3.The merged data products show major improvements in spatial and temporal coverage from the addition of MERSI.The average daily coverage of merged products is approximately 24% of the global ocean and increases by approximately 9% when MERSI data are added in the merging process.Sampling frequency（temporal coverage） is greatly improved by combining MERSI data,with the median sampling frequency increasing from 15.6%（57 d/a） to 29.9%（109 d/a）.The merged Chl a products herein were validated by in situ measurements and comparing them with the merged products using the same approach except for omitting MERSI and Glob Colour and MEa SUREs merged data.Correlation and relative error between the new merged Chl a products and in situ observation are stable relative to the results of the merged products without the addition of MERSI.Time series of the Chl a concentration anomalies are similar to the merged products without adding MERSI and single sensors.The new merged products agree within approximately 10% of the merged Chl a product from Glob Colour and MEa SUREs.展开更多
Recently, numerous novel algorithms have been proposed in the fields of steganography and visual cryptography with the goals of improving security, reliability, and efficiency. Steganography detection is a technique t...Recently, numerous novel algorithms have been proposed in the fields of steganography and visual cryptography with the goals of improving security, reliability, and efficiency. Steganography detection is a technique to tell whether there are secret messages hidden in images. The performance of a steganalysis system is mainly determined by the method of feature extraction and the architecture selection of the classifier. In this paper, we present a new method Visual Pixel Detection VPD for extract data from a color or a grayscale images. Because the human eye can recognize the hidden information in the image after using this detection. The experimental results show that the proposed method provides a better performance on testing images in comparison with the existing method in attacking Steghide, Outguess and F5.展开更多
基金supported by National Basic Research Program of China (973 Program) (No. 2007CB310800)China Postdoctoral Science Foundation (No. 20090460107 and No. 201003794)
文摘With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.
基金supported by the National Natural Science Foundation of China under Grant No.61272458Shaanxi Provinces Natural Science Basic Research Planning Project under Grant No.2014JM2-6119Yu Lin Industry-Academy-Research Cooperation Project under Grant No.2014CXY-12
文摘In the cloud computing environment, outsourcing service mode of data storage causes the security problem, the reliability of data cannot be guaranteed, and the privacy preservation problem has aroused wide concern. In order to solve the problem of inefficiency and high-complexity caused by traditional privacy preservation methods such as data encryption and access control technology, a privacy preservation method based on data coloring is proposed. The data coloring model is established and the coloring mechanism is adopted to deal with the sensitive data of numerical attributes, and the cloud model similarity measurement based on arithmetic average least-approximability is adopted to authenticate the ownership of privacy data. On the premise of high availability of data, the method strengthens the security of the privacy information. Then, the performance, validity and the parameter errors of the algorithm are quantitatively analyzed by the experiments using the UCI dataset. Under the same conditions of privacy preservation requirements, the proposed method can track privacy leakage efficiently and reduce privacy leakage risks. Compared with the k-anonymity approach, the proposed method enhances the computational time efficiency by 18.5%.
文摘为解决因害虫尺度多样性导致其识别度相对较低的问题,本研究提出了一种基于PP-YOLO(PaddlePaddleYou Only Look once)的农业病虫害识别算法。选取2359个病虫害样本数据集,按照9∶1的比例进行训练集、测试集的划分;选择PP-YOLO模型进行病虫害监测,并利用平均精度mAP(mean average precision)指标进行模型精度评价;探讨PP-YOLO结合数据增强mixup、颜色扭曲法在病虫害中小目标检测上的适用性。结果表明,PP-YOLO模型在病虫害中小目标检测方面mAP达47.4%、26.5%;基于PP-YOLO模型结合数据增强mixup与颜色扭曲后在病虫害中小目标检测上mAP分别提升4.3%、2.9%。总之,PP-YOLO模型可有效检测识别农作物害虫,同时,数据增强mixup与颜色扭曲法可有效提升病虫害的数据样本指标。
基金The National Key R&D Program of China under contract No.2016YFA0600102the National Natural Science Foundation of China under contract Nos 41506203,41476159,41506204,41606197,41471303 and 41706209the Cooperation Project of FIO and KOIST under contract No.PI-2017-03
文摘Understanding the ocean＇s role in the global carbon cycle and its response to environmental change requires a high spatio-temporal resolution of observation.Merging ocean color data from multiple sources is an effective way to alleviate the limitation of individual ocean color sensors（e.g.,swath width and gaps,cloudy or rainy weather,and sun glint） and to improve the temporal and spatial coverage.Since the missions of Sea-Viewing Wide Field-of-View Sensor（Sea Wi FS） and Medium-spectral Resolution Imaging Spectrometer（MERIS） ended on December 11,2010 and May 9,2012,respectively,the number of available ocean color sensors has declined,reducing the benefits of the merged ocean color data with respect to the spatial and temporal coverage.In present work,Medium Resolution Spectral Imager（MERSI）/FY-3 of China is added in merged processing and a new dataset of global ocean chlorophyll a（Chl a） concentration（2000–2015） is generated from the remote sensing reflectance（Rrs（λ）） observations of MERIS,Moderate-resolution imaging spectra-radiometer（MODIS）-AQUA,Visible infrared Imaging Radiometer（VIIRS） and MERSI.These data resources are first merged into unified remote sensing reflectance data,and then Chl a concentration data are inversed using the combined Chl a algorithm of color index-based algorithm（CIA） and OC3.The merged data products show major improvements in spatial and temporal coverage from the addition of MERSI.The average daily coverage of merged products is approximately 24% of the global ocean and increases by approximately 9% when MERSI data are added in the merging process.Sampling frequency（temporal coverage） is greatly improved by combining MERSI data,with the median sampling frequency increasing from 15.6%（57 d/a） to 29.9%（109 d/a）.The merged Chl a products herein were validated by in situ measurements and comparing them with the merged products using the same approach except for omitting MERSI and Glob Colour and MEa SUREs merged data.Correlation and relative error between the new merged Chl a products and in situ observation are stable relative to the results of the merged products without the addition of MERSI.Time series of the Chl a concentration anomalies are similar to the merged products without adding MERSI and single sensors.The new merged products agree within approximately 10% of the merged Chl a product from Glob Colour and MEa SUREs.
文摘Recently, numerous novel algorithms have been proposed in the fields of steganography and visual cryptography with the goals of improving security, reliability, and efficiency. Steganography detection is a technique to tell whether there are secret messages hidden in images. The performance of a steganalysis system is mainly determined by the method of feature extraction and the architecture selection of the classifier. In this paper, we present a new method Visual Pixel Detection VPD for extract data from a color or a grayscale images. Because the human eye can recognize the hidden information in the image after using this detection. The experimental results show that the proposed method provides a better performance on testing images in comparison with the existing method in attacking Steghide, Outguess and F5.
