A theoretical study of the multigrid three-dimensional variational data assimilation scheme using a simple bilinear interpolation algorithm

In order to solve the so-called ＂bull-eye＂ problem caused by using a simple bilinear interpolation as an observational mapping operator in the cost function in the multigrid three-dimensional variational （3DVAR） data assimilation scheme, a smoothing term, equivalent to a penalty term, is introduced into the cost function to serve as a means of troubleshooting. A theoretical analysis is first performed to figure out what on earth results in the issue of ＂bull-eye＂, and then the meaning of such smoothing term is elucidated and the uniqueness of solution of the multigrid 3DVAR with the smoothing term added is discussed through the theoretical deduction for one-dimensional （1D） case, and two idealized data assimilation experiments （one- and two-dimensional （2D） cases）. By exploring the relationship between the smoothing term and the recursive filter theoretically and practically, it is revealed why satisfied analysis results can be achieved by using such proposed solution for the issue of the multigrid 3DVAR.

Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.

A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform

Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret image.We suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold transform.The notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in payload.By scrambling the cover image,Arnold transform adds security to the information that gets embedded and also allows embedding more information in each iteration.The hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure system.The proposed method employs a set of keys to ensure that information cannot be decoded by an attacker.The experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats,including medical images.Various performance metrics proves that the retrieved cover image and hidden image are both intact.This System is proven to withstand rotation attack as well.

Improved Dual-image Quality with Reversible Data Hiding Using Translocation and Switching Strategy

Dual-image reversible data hiding(RDH)is a technique for hiding important messages.This technology can be used to safely deliver secret mes-sages to the recipient through dual images in an open network without being easily noticed.The recipient of the image must receive the two stego-images before the secret message can be completely retrieved.Imperceptibility is one of the main advantages of data hiding technology;to increase the imperceptibility,the quality requirements of the stego-images are relatively important.A dual ste-ganographic image RDH method,called a DS-CF scheme that can achieve a bet-ter steganographic image quality using the center folding(CF)strategy.In this paper,we developed a translocation and switching strategy(TaS)to shorten the distances between the stego-pixel coordinates and the cover pixel coordinates after information being hidden.Compared with the DS-CF scheme,our proposed DS-TaS scheme can effectively improve the quality of the steganographic images at the same level of embedding capability.The experimental results show that the PSNR of our DS-TaS scheme at k=1 was 55.66 dB,which is an increase of 1.5 dB,and is 51.43 dB for k=2,46.66 dB for k=3,and 40.91 dB for k=4.In addition,the PSNR values of the stego images was increased by 1.5,0.29,0.29,and 0.19 dB,respectively.This shows that our proposed dual-image RDH method can optimize the visual quality of the stego-images and is better than many other dual-image RDH techniques.

Design and Realization of Block Level Augmented Reality Three-Dimensional Map

In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those provide references and ideas for the later large-scale production of augmented reality three-dimensional map. The augmented reality three-dimensional map is produced based on skyline software. Including the map browsing, measurement and analysis and so on, the basic function of three-dimensional map is realized. The special functional module including housing management, pipeline management and so on is developed combining the need of residential quarters development, that expands the application fields of augmented reality three-dimensional map. Those lay the groundwork for the application of augmented reality three-dimensional map. .

Inhibition of mammalian target of rapamycin induces phenotypic reversion in three-dimensional cultures of malignant breast epithelial cells

Inhibition of mammalian target of rapamycin (m- TOR) is a potential method for cancer treatment. Effects of rapamycin (RAP) on the reversion of malignant breast epithelial cells were investigated on three-dimensional (3D) basement membrane extract (BME) cultures. Through continuous exposure to 20 nM of RAP, cell colony size was significantly reduced in 3D BME cultures of malignant breast epithelial cells, while normal cell colony size appeared unaffected. In unfixed 3D BME cultures of normal and RAP-treated malignant breast epithelial cells, the presence of luminal cell death was confirmed by ethidium bromide and propidium iodide labeling. Increased structural organization was observed by im- munofluorescence staining of F-actin and β-catenin in RAP-treated malignant breast epithelial cells. In monolayer cultures of normal and malignant breast epithelial cells, continuous exposure to 20 nM of RAP increased caspase 3/7 activity and decreased proliferation. Reverse transcriptase polymerase ch- ain reaction (RT-PCR) array analysis indicated a fold increase in the expression of a number of proteins related to polarity, cell-cell adhesion, and cell-matrix adhesion in the presence of RAP. Our data showed that phenotypic reversion of malignancy can be ach- ieved through RAP exposure on 3D BME cultures. This 3D BME culture system will provide correct microenvironments for observing the effects of other mTOR inhibitors on phenotypic reversion of malignant breast epithelial cells.

VISUALIZATION OF THREE-DIMENSIONAL DATA FIELD AND ITS APPLICATION IN MACHINE TESTING

In order to realize visualization of three-dimensional data field （TDDF） in instrument, two methods of visualization of TDDF and the usual manner of quick graphic and image processing are analyzed. And how to use OpenGL technique and the characteristic of analyzed data to construct a TDDF, the ways of reality processing and interactive processing are described. Then the medium geometric element and a related realistic model are constructed by means of the first algorithm. Models obtained for attaching the third dimension in three-dimensional data field are presented. An example for TDDF realization of machine measuring is provided. The analysis of resultant graphic indicates that the three-dimensional graphics built by the method developed is featured by good reality, fast processing and strong interaction

Neural network approach for modification and fitting of digitized data in reverse engineering~

Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF neural network approach to modify and fit the digitized data. The centers for the RBF are selected by using the orthogonal least squares learning algorithm. A mathematically known surface is used for generating a number of samples for training the networks. The trained networks then generated a number of new points which were compared with the calculating points from the equations. Moreover, a series of practice digitizing curves are used to test the approach. The results showed that this approach is effective in modifying and fitting digitized data and generating data points to reconstruct the surface model.

不确定数据上的Reverse Top-k查询

传统的top-k查询为顾客返回符合其偏好的产品集合,reverse top-k查询则返回将给定产品作为top-k结果的偏好集合。reverse top-k查询由于能帮助生产者评估产品对顾客的影响,因此在商业分析中具有重要价值。现有的reverse top-k查询假设数据是精确的,许多现实应用中,数据的不确定性广泛存在。将reverse top-k查询扩展到不确定数据上,并给出了基于物化视图的高效查询算法GMV。实验结果表明,GMV算法能够减少需要计算的偏好数量,具有较高的计算效率。

On the Privacy-Preserving Outsourcing Scheme of Reversible Data Hiding over Encrypted Image Data in Cloud Computing

Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the cloud.In the meantime,some computationally expensive tasks are also undertaken by cloud servers.However,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data.Recently,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data.In this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted domain.The former is that additional bits are extracted after decryption and the latter is that extracted before decryption.Meanwhile,a combined scheme is also designed.This paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different stages.Theoretical analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed scheme.The computation cost of the proposed scheme is acceptable and adjusts to different security levels.

Reversible Data Hiding in Encrypted Image Based on Block Classification Permutation

Recently,reversible data hiding in encrypted image(RDHEI)has attracted extensive attention,which can be used in secure cloud computing and privacy protection effectively.In this paper,a novel RDHEI scheme based on block classification and permutation is proposed.Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively.After block classification,content owner utilizes a specific encryption method,including stream cipher encryption and block permutation to protect image content securely.For the encrypted image,data hider embeds additional secret information in the most significant bits(MSB)of the encrypted pixels in smooth blocks and the final marked image can be obtained.At the receiver side,secret data will be extracted correctly with data-hiding key.When receiver only has encryption key,after stream cipher decryption,block scrambling decryption and MSB error prediction with threshold,decrypted image will be achieved.When data hiding key and encryption key are both obtained,receiver can find the smooth and non-smooth blocks correctly and MSB in smooth blocks will be predicted correctly,hence,receiver can recover marked image losslessly.Experimental results demonstrate that our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.

Local binary pattern-based reversible data hiding

A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.

Constructing Three-Dimension Space Graph for Outlier Detection Algorithms in Data Mining

Outlier detection has very important applied value in data mining literature. Different outlier detection algorithms based on distinct theories have different definitions and mining processes. The three-dimensional space graph for constructing applied algorithms and an improved GridOf algorithm were proposed in terms of analyzing the existing outlier detection algorithms from criterion and theory. Key words outlier - detection - three-dimensional space graph - data mining CLC number TP 311. 13 - TP 391 Foundation item: Supported by the National Natural Science Foundation of China (70371015)Biography: ZHANG Jing (1975-), female, Ph. D, lecturer, research direction: data mining and knowledge discovery.

An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

To measure the security for hot searched reversible data hiding(RDH)technique,especially for the common-used histogram-shifting based RDH(denoted as HS-RDH),several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image.However,conventional steganalysis schemes focused on the previous RDH algorithms,i.e.,some early spatial/pixel domain-based histogram-shifting(HS)schemes,which might cause great changes in statistical characteristics and thus be easy to be detected.For recent improved methods,such as some adaptive prediction error(PE)based embedding schemes,those conventional schemes might be invalid,since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of steganalysis.Therefore,a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for RDH.The contributions of this paper could be summarized as follows.(1)By analyzing the characteristics for those adaptive HS-RDH,an effective“flat ground”based detection method is designed to fast identify whether the given image is used to hide secret data;(2)According to the empirical statistical model,double check mechanism is provided to improve the detection accuracy;(3)In addition,to further improve detection ability,some detailed information for secret data,i.e.,its content and embedding location are further estimated.Compared with conventional steganalysis methods,experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.

Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Reversible data hiding in encrypted images(RDH-EI)technology is widely used in cloud storage for image privacy protection.In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images,we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling.First,the prediction error image is obtained by a novel prediction method before encryption.Then,the image pixel values are divided into two categories by the threshold range,which is selected adaptively according to the image content.Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged.The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm.Moreover,the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position.Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-of-the-art methods for images with different texture complexity.

Reversible Data Hiding Based on Pixel-Value-Ordering and Pixel Block Merging Strategy

With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding performance and the block size.Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data;the smaller the pixel blocks,greater is the embedding capacity.However,it tends to result in the deterioration of the quality of the marked image.Herein,a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method,which realizes the dynamic control of block size by considering the image texture.First,the cover image is divided into non-overlapping 2×2 pixel blocks.Subsequently,according to their complexity,similarity and thresholds,these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4,4×2,or 4×4 sized blocks.Hence,smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio.Experimental results prove that the proposed method is superior to the other three advanced methods.It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.

(r,QI)-Transform：Reversible Data Anonymity Based on Numeric Type of Data in Outsourced Database

An outsource database is a database service provided by cloud computing companies.Using the outsource database can reduce the hardware and software＇s cost and also get more efficient and reliable data processing capacity.However,the outsource database still has some challenges.If the service provider does not have sufficient confidence,there is the possibility of data leakage.The data may has user＇s privacy,so data leakage may cause data privacy leak.Based on this factor,to protect the privacy of data in the outsource database becomes very important.In the past,scholars have proposed k-anonymity to protect data privacy in the database.It lets data become anonymous to avoid data privacy leak.But k-anonymity has some problems,it is irreversible,and easier to be attacked by homogeneity attack and background knowledge attack.Later on,scholars have proposed some studies to solve homogeneity attack and background knowledge attack.But their studies still cannot recover back to the original data.In this paper,we propose a data anonymity method.It can be reversible and also prevent those two attacks.Our study is based on the proposed r-transform.It can be used on the numeric type of attributes in the outsource database.In the experiment,we discussed the time required to anonymize and recover data.Furthermore,we investigated the defense against homogeneous attack and background knowledge attack.At the end,we summarized the proposed method and future researches.

Reversible Data Hiding for Medical Images in Cloud Computing Environments Based on Chaotic Hénon Map

Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In this paper, we combine reversible data hiding with the chaotic Henon map as an encryption technique to achieve an acceptable level of confidentiality in cloud computing environments. And, Haar digital wavelet transformation （HDWT） is also applied to convert an image from a spatial domain into a frequency domain. And then the decimal of coefficients and integer of high frequency band are modified for hiding secret bits. Finally, the modified coefficients are inversely transformed to stego-images.

Reversible Data Embedding Scheme Using Relationships between Pixel-Values and Their Neighbors

Reversible data embedding is becoming a very important issue in securing images transmitted over the Internet, especially in dealing with sensitive images such as those created for military data and medical data. Based on the relationships between pixels and their neighbors, we propose a reversible data embedding scheme to embed hidden messages into an original image. In our proposed scheme, a two-layer data embedding approach is used for our proposed data embedding phase. Layer-1 embedding is used to hide secret data. Layer-2 embedding, which is an embedding variant of the proposed layer-1 embedding, is used to hide side information, such as the parameters required to restore the marked image. In our layer-1 embedding, the value of an embedded pixel is determined according to a predetermined threshold and the relationship between the pixel and its neighbors. In our layer-2 embedding, the similar data embedding concept is expanded to the block-based. Experimental results provide supportive data to show that the proposed scheme can provide greater hiding capacity while preserving the image quality of a marked image in comparison with previous work.

Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery

To improve the security and quality of decrypted images,this work proposes a reversible data hiding in encrypted image based on iterative recovery.The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR(XOR),which improves the security of encrypted images.And then,a pixel-typemark generation method based on block-compression is designed to reduce the extra burden of key management and transfer.At last,an iterative recovery strategy is proposed to optimize the marked decrypted image,which allows the original image to be obtained only using the encryption key.The proposed reversible data hiding scheme in encrypted image is not vulnerable to the ciphertext-only attack due to the fact that the XOR-encrypted pixels are scrambled in the corresponding encrypted image.Experimental results demonstrate that the decrypted images obtained by the proposed method are the same as the original ones,and the maximum embedding rate of proposed method is higher than the previously reported reversible data hiding methods in encrypted image.