本研究报道沙棘(胡颓子科)对雄性白化病白鼠由铬诱导氧化胁迫的抗氧化活性。氧化胁迫通过对小白鼠进行强制性喂养30 d,以浓度为30 m g/kg的铬相对体重比例的重铬酸钾盐实现。铬促使体重下降,而却明显增加了器官与体重比例。铬处理明显...本研究报道沙棘(胡颓子科)对雄性白化病白鼠由铬诱导氧化胁迫的抗氧化活性。氧化胁迫通过对小白鼠进行强制性喂养30 d,以浓度为30 m g/kg的铬相对体重比例的重铬酸钾盐实现。铬促使体重下降,而却明显增加了器官与体重比例。铬处理明显减少了谷胱甘肽的降低,增加了丙二醛和肌氨酸磷酸激酶的水平;而且它还加强了血清中谷氨酸草酰乙酸转移酶和谷氨酸丙酮酸转移酶的浓度。用不同剂量的沙棘叶子提取物(乙醇提取)对保护铬元素诱导的氧化胁迫进行了评估,结果表明叶子提取物在浓度为100到250 m g/kg铬与体重比的情况下可以明显保护动物避免由铬所诱导的氧化伤害。展开更多
Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis st...Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem. A critical part of the steganalyser design depends on the selection of informative features. This paper is aimed at proposing a novel attack with improved performance indices with the following implications： 1） employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, as compared to other conventional wavelet transforms; 2） increasing the sensitivity and specificity of the system by the feature reduction phase; 3） realizing the system using an efficient classification engine, a neuro-C4.5 classifier, which provides better classification rate. An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.展开更多
A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This s...A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This scheme is made adaptive using the correlation of the neighboring pixels.Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image.The size of the embedded data depends on the statistics of the pixel distribution in the cover image.One of the major issues in reversible data embedding,the location map,is minimized because of the interpolation process.This technique,which is actually LSB matching,embeds only the residuals of modulo radix into the LSBs of each pixel.No attacks on this RDH technique will be able to decode the hidden content in the marked image.The proposed scheme delivers a prominent visual quality despite high embedding capacity.Experimental tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing schemes.Since the algorithm is based on the variable radix number system,it is more resistant to most of the steganographic attacks.The results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio(PSNR),Embedding Capacity(EC)and Structural Similarity Index Metric(SSIM).展开更多
A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this wor...A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label data.This work proposes a framework of two phases.The imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1.Later,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase 2.The proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional MLSMOTE.The minority labels in these samples are called difficult minority labels and are more prone to penalize classification performance.The concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision boundary.In phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic samples.Experimentation on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.展开更多
文摘本研究报道沙棘(胡颓子科)对雄性白化病白鼠由铬诱导氧化胁迫的抗氧化活性。氧化胁迫通过对小白鼠进行强制性喂养30 d,以浓度为30 m g/kg的铬相对体重比例的重铬酸钾盐实现。铬促使体重下降,而却明显增加了器官与体重比例。铬处理明显减少了谷胱甘肽的降低,增加了丙二醛和肌氨酸磷酸激酶的水平;而且它还加强了血清中谷氨酸草酰乙酸转移酶和谷氨酸丙酮酸转移酶的浓度。用不同剂量的沙棘叶子提取物(乙醇提取)对保护铬元素诱导的氧化胁迫进行了评估,结果表明叶子提取物在浓度为100到250 m g/kg铬与体重比的情况下可以明显保护动物避免由铬所诱导的氧化伤害。
文摘Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem. A critical part of the steganalyser design depends on the selection of informative features. This paper is aimed at proposing a novel attack with improved performance indices with the following implications： 1） employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, as compared to other conventional wavelet transforms; 2） increasing the sensitivity and specificity of the system by the feature reduction phase; 3） realizing the system using an efficient classification engine, a neuro-C4.5 classifier, which provides better classification rate. An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.
文摘A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This scheme is made adaptive using the correlation of the neighboring pixels.Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image.The size of the embedded data depends on the statistics of the pixel distribution in the cover image.One of the major issues in reversible data embedding,the location map,is minimized because of the interpolation process.This technique,which is actually LSB matching,embeds only the residuals of modulo radix into the LSBs of each pixel.No attacks on this RDH technique will be able to decode the hidden content in the marked image.The proposed scheme delivers a prominent visual quality despite high embedding capacity.Experimental tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing schemes.Since the algorithm is based on the variable radix number system,it is more resistant to most of the steganographic attacks.The results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio(PSNR),Embedding Capacity(EC)and Structural Similarity Index Metric(SSIM).
基金partly supported by the Technology Development Program of MSS(No.S3033853)by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A4A1031509).
文摘A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label data.This work proposes a framework of two phases.The imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1.Later,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase 2.The proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional MLSMOTE.The minority labels in these samples are called difficult minority labels and are more prone to penalize classification performance.The concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision boundary.In phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic samples.Experimentation on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.
