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.

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

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.

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.

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.

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.

基于word embedding的短文本特征扩展与分类

近几年短文本的大量涌现,给传统的自动文本分类技术带来了挑战.针对短文本特征稀疏、特征覆盖率低等特点,提出了一种基于word embedding扩展短文本特征的分类方法.word embedding是一种词的分布式表示,表示形式为低维连续的向量形式,并且好的word embedding训练模型可以编码很多语言规则和语言模式.本文利用word embedding空间分布特点和其蕴含的线性规则提出了一种新的文本特征扩展方法.结合扩展特征我们分别在谷歌搜索片段、中国日报新闻摘要两类数据集上进行了短文本分类实验,对比于仅使用词袋表示文本特征的分类方法,准确率分别提高:8.59%,7.42%.

基于Word Embedding语义相似度的字母缩略术语消歧

该文提出基于Word Embedding的歧义词多个义项语义表示方法,实现基于知识库的无监督字母缩略术语消歧。方法分两步聚类,首先采用显著相似聚类获得高置信度类簇,构造带有语义标签的文档集作为训练数据。利用该数据训练多份Word Embedding模型,以余弦相似度均值表示两个词之间的语义关系。在第二步聚类时,提出使用特征词扩展和语义线性加权来提高歧义分辨能力,提高消歧性能。该方法根据语义相似度扩展待消歧文档的特征词集合,挖掘聚类文档中缺失的语义信息,并使用语义相似度对特征词权重进行线性加权。针对25个多义缩略术语的消歧实验显示,特征词扩展使系统F值提高约4%,使用语义线性加权后F值再提高约2%,达到89.40%。

基于位置敏感Embedding的中文命名实体识别

在基于条件随机场的中文命名实体识别任务中,现有表示学习方法学习到的特征存在语义表示偏差,给中文命名实体识别带来噪声。针对此问题,提出了一种基于位置敏感Embedding的中文命名实体识别方法。该方法将上下文位置信息融入到现有的Embedding模型中,采用多尺度聚类方法抽取不同粒度的Embedding特征,通过条件随机场来识别中文命名实体。实验证明,该方法学习到的特征缓解了语义表示偏差,进一步提高了现有系统的性能,与传统方法相比,F值提高了2.85%。

Locally linear embedding-based seismic attribute extraction and applications

How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.

CATGUT EMBEDDING FOR TREATMENT OF SIMPLE OBESITY COMPLICATED WITH ABNORMAL BLOOD FAT

Objective To compare the therapeutic effect of catgut embedding combined with diet plus exercise with that of diet plus exercise in treating simple obesity complicated with abnormal blood fat. Methods A prescription of diet plus exercise was given to the patients in a control group, and catgut embedding was added to the prescription in a treatment group. It was given once every 15 days for the 1st three treatments, and once every month for the 2^nd three treatments. Six treatments constituted a therapeutic course. Body weight and blood fat were measured before and half a year since treatment. Results After treatment, BMI, TO, TG and LDL were very significantly improved in the treatment group （P〈0.01）, and BMI and TG were very significantly （P〈0.01） and TO and LDL were significantly （P〈0.05） improved in the control group. HDL was not obviously improved in both groups （P〉0.05）. The improvement of TO, TG and LDL in the treatment group was significantly more than that in the control group （P〈0.01）, but no difference in improvement of BMI and HDL was found between the two groups （P〉0.05）. Conclusion The therapy of diet plus exercise was effective in body weight reduction and lowering blood fat in patients with simple obesity complicated with abnormal blood fat, and catgut embedding used in combination was even better in lowering blood fat.

PATHS AND CYCLES EMBEDDING ON FAULTY ENHANCED HYPERCUBE NETWORKS

Let Qn,k （n 〉 3, 1 〈 k ≤ n - 1） be an n-dimensional enhanced hypercube which is an attractive variant of the hypercube and can be obtained by adding some complementary edges, fv and fe be the numbers of faulty vertices and faulty edges, respectively. In this paper, we give three main results. First, a fault-free path P[u, v] of length at least 2n - 2fv - 1 （respectively, 2n - 2fv - 2） can be embedded on Qn,k with fv ＋ f≤ n- 1 when dQn,k （u, v） is odd （respectively, dQ,~,k （u, v） is even）. Secondly, an Q,,k is （n - 2） edgefault-free hyper Hamiltonianaceable when n （ 3） and k have the same parity. Lastly, a fault-free cycle of length at least 2n - 2fv can be embedded on Qn,k with f~ 〈 n - 1 and fv＋f≤2n-4.

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.

A Survey of Embedding Algorithm for Virtual Network Embedding

Network virtualization(NV) is pushed forward by its proponents as a crucial attribute of next generation network, aiming at overcoming the gradual ossification of current networks, particularly to the worldwide Internet. Through virtualization, multiple customized virtual networks(VNs), requested by users, are allowed to coexist on the underlying substrate networks(SNs). In addition, the virtualization scheme contributes to sharing underlying physical resources simultaneously and seamlessly. However, multiple technical issues still stand in the way of NV successful implementation. One key technical issue is virtual network embedding(VNE), known as the resource allocation problem for NV. This paper conducts a survey of embedding algorithms for VNE problem. At first, the NV business model for VNE problem is presented. Then, the latest VNE problem description is presented. Main performance metrics for evaluating embedding algorithms are also involved. Afterwards, existing VNE algorithms are detailed, according to the novel proposed category approach. Next, key future research aspects of embedding algorithms are listed out. Finally, the paper is briefly concluded.

基于Word Embedding的遥感影像检测分割

遥感影像检测分割技术通常需提取影像特征并通过深度学习算法挖掘影像的深层特征来实现.然而传统特征(如颜色特征、纹理特征、空间关系特征等)不能充分描述影像语义信息,而单一结构或串联算法无法充分挖掘影像的深层特征和上下文语义信息.针对上述问题,本文通过词嵌入将空间关系特征映射成实数密集向量,与颜色、纹理特征的结合.其次,本文构建基于注意力机制下图卷积网络和独立循环神经网络的遥感影像检测分割并联算法(Attention Graph Convolution Networks and Independently Recurrent Neural Network,ATGIR).该算法首先通过注意力机制对结合后的特征进行概率权重分配;然后利用图卷积网络(GCNs)算法对高权重的特征进一步挖掘并生成方向标签,同时使用独立循环神经网络(IndRNN)算法挖掘影像特征中的上下文信息,最后用Sigmoid分类器完成影像检测分割任务.以胡杨林遥感影像检测分割任务为例,我们验证了提出的特征提取方法和ATGIR算法能有效提升胡杨林检测分割任务的性能.

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.

A PSO Based Multi-Domain Virtual Network Embedding Approach

This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.

Research on Embedding Capacity and Efficiency of Information Hiding Based on Digital Images

Generally speaking, being an efficient information hiding scheme, what we want to achieve is high embedding capacity of the cover image and high visual quality of the stego image, high visual quality is also called embedding efficiency. This paper mainly studies on the information hiding technology based on gray-scale digital images and especially considers the improvement of embedding capacity and embedding efficiency. For the purpose of that, two algorithms for information hiding were proposed, one is called high capacity of information hiding algorithm (HCIH for short), which achieves high embedding rate, and the other is called high quality of information hiding algorithm (HQIH for short), which realizes high embedding efficiency. The simulation experiments show that our proposed algorithms achieve better performance.

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.