A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

A Linked List Encryption Scheme for Image Steganography without Embedding

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

TCM-HIN2Vec:A strategy for uncovering biological basis of heart qi deficiency pattern based on network embedding and transcriptomic experiment

Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD pattern genes using the new strategy,TCM-HIN2Vec,which involves heterogeneous network embedding and transcriptomic experiments.First,a heterogeneous network of traditional Chinese medicine(TCM)patterns was constructed using public databases.Next,we predicted HQD pattern genes using a heterogeneous network-embedding algorithm.We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq.Finally,we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis.Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism,signal transduction pathways,and immune processes.Moreover,we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern.Furthermore,herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD.Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes,but also deciphering the basis of HQD pattern.Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns,leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.

Insider threat detection approach for tobacco industry based on heterogeneous graph embedding

In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.

Thin-Shell Wormholes Admitting Conformal Motions in Spacetimes of Embedding Class One

This paper discusses the feasibility of thin-shell wormholes in spacetimes of embedding class one admitting a one-parameter group of conformal motions. It is shown that the surface energy density σis positive, while the surface pressure is negative, resulting in , thereby signaling a violation of the null energy condition, a necessary condition for holding a wormhole open. For a Morris-Thorne wormhole, matter that violates the null energy condition is referred to as “exotic”. For the thin-shell wormholes in this paper, however, the violation has a physical explanation since it is a direct consequence of the embedding theory in conjunction with the assumption of conformal symmetry. These properties avoid the need to hypothesize the existence of the highly problematical exotic matter.

Effect of reductant type on the embedding direct reduction of beach titanomagnetite concentrate

Iron and titanium were recovered from beach titanomagnetite(TTM) concentrate by embedding direct reduction and magnetic separation. The reduction products and the effects of the reductant type and reduction temperature on the reduction behavior were investigated. The results showed that the reduction of TTM concentrate was strongly related to the gasification reactivity of the reductant. Bitumite presented a better product index than wheat-straw biochar and coke, mainly because the gasification reactivity of bitumite was better than that of the other reductants. In addition, high temperatures were not beneficial to embedding direct reduction because of the emergence of a molten phase and iron-joined crystals, which in turn reduced the diffusion rate of the reducing gas and impeded the reduction reaction in the central area of the roasted briquette. The use of bitumite as the reductant at a C/Fe molar ratio of 1.4 and a reduction temperature of 1200°C for 120 min resulted in direct-reduction iron powder assaying 90.28 wt% TFe and 0.91 wt% TiO_2 with an iron recovery of 91.83% and titanium concentrate assaying 46.01 wt% TiO_2 with a TiO_2 recovery of 91.19%. Titanium existed mainly in the form of anosovite and ilmenite in the titanium concentrate.

基于Hessian局部线性嵌入和MLP-Mixer的液体火箭发动机涡轮泵轻量化故障诊断框架

作为液体火箭发动机推进剂输送系统的关键部件,涡轮泵的运行状态直接影响着整个运载系统的性能,然而,现有的故障诊断方法往往面临特性参数选择片面及计算复杂度高等问题。针对上述局限,提出了面向涡轮泵的轻量化故障诊断框架。所提方法利用Hessian局部线性嵌入算法对信号时域、频域及时频特征进行降维,并引入一种轻量化的深度学习模型MLP-Mixer作为分类器,进而实现不同故障状态的辨识。采用某型号涡轮泵试车数据验证了所提方法的有效性,结果表明,该方法能够在保障诊断精度的同时有效降低计算复杂度,提高诊断效率。

Heuristic Solutions of Virtual Network Embedding： A Survey

Network virtualization(NV) is considered as an enabling tool to remove the gradual ossification of current Internet. In the network virtualization environment, a set of heterogeneous virtual networks(VNs), isolated from each other, share the underlying resources of one or multiple substrate networks(SNs) according to the resource allocation strategy. This kind of resource allocation strategy is commonly known as so called Virtual Network Embedding(VNE) algorithm in network virtualization. Owing to the common sense that VNE problem is NP-hard in nature, most of VNE algorithms proposed in the literature are heuristic. This paper surveys and analyzes a number of representative heuristic solutions in the literature. Apart from the analysis of representative heuristic solutions, a taxonomy of the heuristic solutions is also presented in the form of table. Future research directions of VNE, especially for the heuristics, are emphasized and highlighted at the end of this survey.

Virtual 5G Network Embedding in a Heterogeneous and Multi-Domain Network Infrastructure

The pursuit of the higher performance mobile communications forces the emergence of the fifth generation mobile communication(5G). 5G network, integrating wireless and wired domain, can be qualified for the complex virtual network work oriented to the cross-domain requirement. In this paper, we focus on the multi-domain virtual network embedding in a heterogeneous 5G network infrastructure, which facilitates the resource sharing for diverse-function demands from fixed/mobile end users. We proposed the mathematical ILP model for this problem.And based on the layered-substrate-resource auxiliary graph and an effective six-quadrant service-type-judgment method, 5G embedding demands can be classified accurately to match different user access densities. A collection of novel heuristic algorithms of virtual 5G network embedding are proposed. A great deal of numerical simulation results testified that our algorithm performed better in terms of average blocking rate, routing latency and wireless/wired resource utilization, compared with the benchmark.

Using Network Embedding to Obtain a Richer and More Stable Network Layout for a Large Scale Bibliometric Network

Purpose: The goal of this study is to explore whether deep learning based embed ded models can provide a better visualization solution for large citation networks. De sign/methodology/approach: Our team compared the visualization approach borrowed from the deep learning community with the well-known bibliometric network visualization for large scale data. 47,294 highly cited papers were visualized by using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic Open Ord method with different edge cutting strategies and parameters. Findings: The network embedded maps with t-SNE preserve a very similar global structure to the full edges classic force-directed map, while the maps vary in local structure. Among them, the Node2Vec model has the best overall visualization performance, the local structure has been significantly improved and the maps' layout has very high stability.Research limitations: The computational and time costs of training are very high for network em bedded models to obtain high dimensional latent vector. Only one dimensionality reduction technique was tested. Practical implications: This paper demonstrates that the network embedding models are able to accurately reconstruct the large bibliometric network in the vector space. In the future, apart from network visualization, many classical vector-based machine learning algorithms can be applied to network representations for solving bibliomet ric analysis tasks. Originality/value: This paper provides the first systematic comparison of classical science mapping visualization with network embedding based visualization on a large scale dataset. We showed deep learning based network embedding model with t-SNE can provide a richer,more stable science map. We also designed a practical evaluation method to investigate and compare maps.

Uncertainty Theory Based Novel Multi-Objective Optimization Technique Using Embedding Theorem with Application to R &D Project Portfolio Selection

This paper introduces a novice solution methodology for multi-objective optimization problems having the coefficients in the form of uncertain variables. The embedding theorem, which establishes that the set of uncertain variables can be embedded into the Banach space C[0, 1] × C[0, 1] isometrically and isomorphically, is developed. Based on this embedding theorem, each objective with uncertain coefficients can be transformed into two objectives with crisp coefficients. The solution of the original m-objectives optimization problem with uncertain coefficients will be obtained by solving the corresponding 2 m-objectives crisp optimization problem. The R & D project portfolio decision deals with future events and opportunities, much of the information required to make portfolio decisions is uncertain. Here parameters like outcome, risk, and cost are considered as uncertain variables and an uncertain bi-objective optimization problem with some useful constraints is developed. The corresponding crisp tetra-objective optimization model is then developed by embedding theorem. The feasibility and effectiveness of the proposed method is verified by a real case study with the consideration that the uncertain variables are triangular in nature.

A new method of determining the optimal embedding dimension based on nonlinear prediction

A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.

EMBEDDING THEOREMS ON CAMPANATO-MORREY SPACES FOR VECTOR FIELDS OF HoRMANDER TYPE

This paper deals with embedding theorems on Campanato-Marrey spaces formed by degenerate vector fields, which include Honnander and Grushin type of vector fields. These embedding theorems are somewhat different from the known Poincare estimates. The main ingredients of the proofs rely on the fractional maximal functions. These results evidently have applications to the regularity of subelliptic PDE.

Nash Embedding of Witten’s M-Theory and the Hawking-Hartle Quantum Wave of Dark Energy

Euclidean embedding of the 11-dimensional M-theory turned out to require a very large space leaving lavish amounts of 242 dimensional pseudo truly empty “regions” devoid of space and time and consequently of anything resembling ordinary physical energy density. It is shown here using Nash embedding that the ratio of “solid” M-theory spacetime to its required embedding “non-spacetime” is 1/22 for a classical theory and 1/22.18033989 for an analogous fractal theory. This then leads to a maximal ordinary energy density equation equal to that of Einstein’s famous formula E=mc2 but multiplied with in full agreement with previous results obtained using relatively more conventional methods including running the electromagnetic fine structure constant in the exact solution of the hydrogen atom. Consequently, the new equation corresponds to a quantum relativity theory which unlike Einstein’s original equation gives quantitative predictions which agree perfectly with the cosmological measurements of WMAP and the analysis of certain supernova events. Never the less in our view dark energy also exists being the energy of the quantum wave amounting to 95.5 present of the total Einstein theoretical energy which is blind to any distinction between ordinary energy of the quantum particle and the dark energy of the quantum wave. However, since measurement leads to the collapse of the Hawking-Hartle quantum wave, dark energy being a quantum wave non-ordinary energy could not possibly be measured in the usual way unless highly refined quantum wave non-demolition technology is developed if possible. It is a further reason that dark energy having a different sign to ordinary energy is the cause behind the anti gravity force which is pushing the universe apart and accelerating cosmic expansion. Consequently it can be seen as the result of anticlastic Cartan-like curvature caused by extra compactified dimensions of spacetime. A simple toy model demonstration of the effect of curvature in a “material” space is briefly discussed.

THE EMBEDDING THEORY OF NATIVE SPACES

In the theory of radial basis functions, mathematicians use linear combinations of the translates of the radial basis functions as interpolants. The set of these linear combinations is a normed vector space. This space can be completed and become a Hilbert space, called native space, which is of great importance in the last decade. The native space then contains some abstract elements which are not linear combinations of radial basis functions. The meaning of these abstract elements is not fully known. This paper presents some interpretations for the these elements. The native spaces are embedded into some well-known spaces. For example, the Sobolev-space is shown to be a native space. Since many differential equations have solutions in the Sobolev-space, we can therefore approximate the solutions by linear combinations of radial basis functions. Moreover, the famous question of the embedding of the native space into L2(Ω) is also solved by the author.

Efficient Virtual Network Embedding Algorithm Based on Restrictive Selection and Optimization Theory Approach

Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One main challenge in NV is virtual network embedding(VNE). VNE is a NPhard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in smallscaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT(Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNERSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNERSOT, a simulation against typical and stateof-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request(VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.

OBSERVATION OF REMOTE AND RECENT THERAPEUTIC EFFECTS OF ACUPOINT-THREAD EMBEDDING, WESTERN MEDICINE OR ALLIED THERAPY ON PERENNIAL ALLERGIC RHINITIS

Objective: The comparison of the remote and recent therapeutic effects on perennial allergic rhinitis was carried on among single application of acupoint-thread embedding, western medicine (certirizine+flixonase) and allied therapy. Method: Ninety cases were randomized into thread-embedding group, western medicine group and embedding+western medicine group (allied-therapy group), 30 cases in each one. In thread embedding group, Yingxiang (迎香 LI 20) and Feishu (肺俞 BL 13) were selected for thread embedding, once a week, totally 4 treatments were required. In western medicine group, certirizine tablet 10 mg in combination with flixonase sprayer was applied once per day, continuously for 30 days in treatment. In allied-therapy group, both of the above methods were adopted continuously for 30 days in treatment. The therapeutic effects were in statistics at the end of treatment and 1 year later. Results: At the end of treatment, in thread-embedding group, 21 cases were remarkably effective, 3 cases effective and 6 cases no effect. In western medicine group, 20 cases were remarkably effective, 3 cases effective and 7 cases no effect. In allied-therapy group, 23 cases were remarkably effective, 2 cases effective and 5 cases no effect. It was shown in statistics that there was no significant difference in therapeutic effects among three groups ( P>0.05 ). But one year after the end of treatment, it was indicated that in thread-embedding group, 3 cases were remarkably effective, 10 cases effective and 17 cases no effect; in western medicine group, 2 cases were remarkably effective, 9 cases effective and 19 cases no effect; in allied-therapy group, 7 cases were remarkably effective, 11 cases effective and 12 cases no effect. The significant difference in the therapeutic effects had not been presented in the comparison between thread-embedding group and western medicine group (P>0.05), but that had been presented between allied-therapy group with either of the groups with single therapy applied (P<0.05). Conclusion: Quite good recent therapeutic effect of either acupoint-thread embedding or western medicine (certirizine+flixonase) was achieved on perennial allergic rhinitis. The remote therapeutic effect of allied therapy was more advantageous than that of single application of either thread embedding or western medicine.

STRONG EMBEDDINGS OF PLANAR GRAPHS ON HIGHER SURFACES

In this paper, the authors discuss the upper bound for the genus of strong embeddings for 3-connected planar graphs on higher surfaces. It is shown that the problem of determining the upper bound for the strong embedding of 3-connected planar near-triangulations on higher non-orientable surfaces is NP-hard. As a corollary, a theorem of Richter, Seymour and Siran about the strong embedding of 3-connected planar graphs is generalized to orientable surface.

Influence of Network Embedding on the Transformation Performance of Original Equipment Manufactures

Based on the embeddedness theory,this paper analyzes the impact of network embedding features and types on the transformation performance of foundry enterprises.Through the investigation and analysis of more than 200 foundry enterprises in Zhejiang Province,it is found that network relationship embedding and structure embedding have positive impacts on transformation of foundry enterprises.Professional embedding and technical embedding have a positive effect in the transformation of foundry enterprises,and knowledge absorption ability has a positive adjustment role in the transformation performance of network embedding foundry enterprises.