Total Embedding Research Articles

Meta-learning framework with updating information flow for enhancing inductive prediction

Inductive relation prediction, which handles unseen entities at the reasoning stage, has the potential to complete continuously expanding knowledge graphs. Existing inductive approaches predominantly focus on providing alternatives predicted by the subgraph structure of the target. However, many approaches neglect the correlation between semantic relationships, which does not mine additional implicit information. In this paper, we propose MUIF, a meta-learning framework with an updated information flow that addresses these challenges. Further, MUIF aims to enhance the role of relational semantics in inductive prediction, while emphasizing the importance of distinguishing relational paths. We propose an updated information flow mechanism for strengthening the relational pass, and we use a contrastive strategy to represent strongly associated paths. Further, meta-learning is applied to transfer the updated total embedding, thereby enhancing prediction accuracy. Further, the Hits@10 of MUIF can reach up to 97.03% in the link prediction experiments, thereby demonstrating the excellent performance of the proposed approach.

Cost-Efficient Cluster Migration of VNFs for Service Function Chain Embedding

Network Function Virtualization (NFV) is a network architecture that separates network functions from dedicated hardware, implementing them as software modules known as Virtual Network Functions (VNFs), which are executed in virtual machines or containers. NFV increases the deployment flexibility and agility within operator networks and reduces the operating and capital expenditures significantly. In NFV, migration of VNFs can significantly reduce the embedding cost. However, stringent latency requirements between VNFs can make them tightly coupled, thus hindering each VNF from being migrated individually, and resulting in poor performance. One of the main challenges in an NFV environment is therefore to migrate a cluster of VNFs to minimize the embedding cost. In this paper, we aim to solve the problem of cluster VNF migration by considering the given inter-VNF latency requirements. We formulate the VNF migration problem as an Integer Linear Programming (ILP) and present two scalable and efficient algorithms for migrating a cluster of VNFs. Through extensive experiments, we show that our proposed algorithms are highly effective. They reduce the total embedding cost by 14% compared to the existing heuristics, while being much more scalable in terms of execution time compared to the brute-force approach.

Integrating Coordinate Transformation and Random Sampling Into High-Capacity Reversible Data Hiding in Encrypted Polygonal Models

Reversible data hiding in encrypted media involves using algorithms to perform data encryption to improve the privacy of the original media and hide data for covert communication or access control. This study explores the feasibility of applying coordinate transformation and random sampling to enhance the embedding rate and total embedding capacity of separable reversible data hiding in encrypted polygonal models based on multiple most significant bit (multi-MSB) prediction and Huffman coding. We first transform each vertex coordinate value of the input model into a decimal value between 0 and 1 and then convert the value into numerous binary digits with a user-defined compression threshold. Thereafter, the random sampling concept is used to generate embeddable vertices with only partial neighboring vertices as a reference. Thus, the embedding rate and total embedding capacity can both be increased considerably. The multi-MSB prediction technique is adopted to obtain the embedding capacity of each embeddable vertex, each vertex coordinate value in the proposed study can have a respective embedding length. Finally, the auxiliary information compressed through Huffman coding and an encrypted secret message are embedded in the multi-MSB of each embeddable vertex coordinate value through bit substitution. The experimental results of this study indicate the feasibility of the proposed algorithm.

High Embedding Capacity Color Image Steganography Scheme Using Pixel Value Differencing and Addressing the Falling-Off Boundary Problem

In this paper, we changed the methodology for pixel value differencing. The proposed method work on RGB color images improves the existing PVD technique in terms of embedding capacity and overcomes the issue of falling off boundaries in the traditional PVD technique, and provides security to the secret message from histogram quantization attack. Color images are composed of three different color channels (red, green, and blue), so we cannot apply the traditional pixel value differencing algorithm to them. Due to that, the proposed technique divides the RGB photograph in red, blue, and green channels. Following that the modified pixel value differencing algorithm is employed to all successive pixels of color channels. We get the total embedding capacity by adding the embedding capacities of each color component. After embedding the data, we concatenate the color channels to get the stegoimage. On a series of color images, we tested our pixel value differencing approach and found that the stego-picture’s visual excellence and payload capacity were reasonable. The variation in histogram between the stego and cover photographs was minor, making it resistant to histogram quantization attacks, and the suggested approach also solves the issue of falling off the boundary.

Total embedding of spermatic cord and hilar soft tissue in orchiectomy for seminoma: does the extensive sampling improve pathologic risk factors?

Pure seminomas represent the majority of testicular germ cell tumors and accurate diagnosis and staging require an accurate sampling of radical orchiectomy specimens. The aim of our study is to find the most informative gross sampling method for orchiectomy specimens. We performed the extensive sampling of 88 radical orchiectomy specimens embedding in their entirety testicular hilum, rete testis, hilar soft tissue, and spermatic cord. We examined the impact of this procedure on tumor stage, prognostic parameters (lymphovascular invasion and infiltration of rete testis, epididymis, tunica vaginalis, and spermatic cord), and their relationship with recurrence. Eighty-eight seminomas from 88 radical orchiectomies were sampled. Seventy-seven cases (87.5%) presented as clinical stage I and 11 cases (12.5%) as clinical stage II. The follow-up period range was 18-54months and 82 patients (93.2%) had a minimum of 2-year follow-up. Tumor size ranged from 0.4 to 16cm (mean 3.6) requiring a mean of 7.1 sections for entire tumoral sampling. Epididymis required 2 to 8 sections (mean 3.3), and hilum and hilar soft tissues 2 to 9 sections (mean 3.4). Epididymal infiltration and lymphovascular invasion resulted significant at multivariate analysis generating a receiver operating characteristic (ROC) curve with area under curve of 0.778. All the other parameters (except for pagetoid rete testis infiltration) were significant to predict metastasis only at univariate analysis. Extensive sampling of radical orchiectomy specimens does not improve the accuracy of staging in pure seminomas. Lymphovascular invasion and epididymal infiltration are useful to predict metastasis.

An Improved Separable and Reversible Steganography in Encrypted Grayscale Images

Reversible data hiding in encrypted images (RDH-EI) has attracted various communities that deal with security. Xiao et al. presented RDH-EI that uses pixel value ordering (PVO) and additive homomorphism for encryption ensuring that the embedding capacity of plain and encrypted image remains relatively same. However, the size of location map required to manage over/under flow blocks increased drastically reducing the overall embedding capacity. In the paper, a new PVO-based separable RDH scheme is proposed using double encryption. The original image is encrypted using chaotic mapping in Phase 1, ensuring that the performance of PVO algorithm in an encrypted domain is similar to that in plain domain. Secret data is embedded in this encrypted image. The embedded image is re-encrypted using additive homomorphism to enhance security. The proposed scheme is more efficient as it improves the pure embedding rate and total embedding capacity of the image by reducing location map size, overcoming the drawback of Xiao et al.

Secure Halftone Image Steganography Based on Feature Space and Layer Embedding.

Syndrome-trellis codes (STCs) are commonly used in image steganographic schemes, which aim at minimizing the embedding distortion, but most distortion models cannot capture the mutual interaction of embedding modifications (MIEMs). In this article, a secure halftone image steganographic scheme based on a feature space and layer embedding is proposed. First, a feature space is constructed by a characterization method that is designed based on the statistics of 4 ×4 pixel blocks in halftone images. Upon the feature space, a generalized steganalyzer with good classification ability is proposed, which is used to measure the embedding distortion. As a result, a distortion model based on a hybrid feature space is constructed, which outperforms some state-of-the-art models. Then, as the distortion model is established on the statistics of local regions, a layer embedding strategy is proposed to reduce MIEM. It divides the host image into multiple layers according to their relative positions in 4 ×4 blocks, and the embedding procedure is executed layer by layer. In each layer, any two pixels are located at different 4 ×4 blocks in the original image, and the distortion model makes sure that the calculation of pixel distortions is independent. Between layers, the pixel distortions of the current layer are updated according to the previous embedding modifications, thus reducing the total embedding distortion. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high statistical security when resisting the state-of-the-art steganalysis.

Numerical modeling of 3D site-city effects including partially embedded buildings using spectral element methods. Application to the case of Viña del Mar city, Chile

In recent years, seismic wave propagation analyses have become a powerful tool to evaluate the site effects in a given region. Among several approaches, Spectral Element Method (SEM) has been widely used with that purpose because its flexibility and computational efficiency. In addition to other effects than basin shape, material nonlinearity and heterogeneity, the multiple interactions between the soil and structures, denominated site-city effects (SCI), can play a crucial role in densely populated areas. There are many options to model this kind of interaction, especially if the buildings are partially embedded on the soil. This paper evaluates the importance of the proper SCI modeling against more standard uncoupled approaches, focusing on the local interaction between the soil and a group of buildings including inelastic soil behavior. We focus our work on the case of downtown Viña del Mar, a touristic coastal city of central Chile, where the observation of a reiterated distribution of damage in reinforced concrete buildings during two major earthquakes has motivated numerous studies. For that purpose, a realistic 3D numerical model of the area is created, considering the existing buildings and using micro-vibration as a main calibration tool. The open-source code SPEED was used to perform the wave propagation simulation, which combines the spectral element method with a discontinuous Galerkin approach. A geophysical study was conducted to estimate the model parameters, shear modulus degradation and damping curves are extracted from laboratory tests to account for the non-linearity of the soil. In general, the results indicate that the inclusion of the SCI is beneficial to the structure's response in most cases, and that SCI modeling needs to considerate the level of embedment to obtain more precise results. Indeed, in buildings of 12 or more stories, the response would not be affected by the level of embedding of the base and the inclusion of site-city effects is beneficial, while for buildings lower than 5 stories, the total embedment of the base generates a significant decrease of the response.

Hodge numbers and deformations of Fano 3-folds

We show that index 1 Fano 3-folds which lie in weighted Grassmannians in their total anticanonical embedding have finite automorphism group, and we relate the deformation theory of any Fano 3-fold that has a K3 elephant to its Hodge theory. Combining these results with standard Gorenstein projection techniques calculates both the number of deformations and the Hodge numbers of most quasi-smooth Fano 3-folds in low codimension. This provides detailed new information for hundreds of families of Fano 3-folds.

Improved piezoelectric properties of PLA/PZT hybrid composite films

Piezoelectric polymer-ceramic composites are promising materials for Sensing, Wireless electronics and actuating applications. We report the fabrication of highly piezoelectric biocompatible films containing lead zirconate titanate (PZT) ferroelectric ceramic particles dispersed in poly lactic acid (PLA) with different volume fractions using a solvent cast technique. The properties of the piezoelectric polymer-ceramic films were investigated by Fourier transform infrared spectrometry (FTIR) and field emission scanning electron microscopy (FE-SEM). In the FTIR spectra appear a large number of absorption bands which are attributed to the phases from PLA matrix confirming the total embedding of PZT filler into the matrix. The SEM results showed a good distribution of fillers in the matrix. We find that the added PZT imposes a significant effect on the α–β phase transformation. Our finding can lead to extraordinary enhancement of piezoelectric properties for the PLA/PZT composite films. Keywords: Piezoelectric polymer-ceramic, PLA, PZT, solvent cast technique.

Adversarially Learned Total Variability Embedding for Speaker Recognition with Random Digit Strings.

Over the recent years, various research has been conducted to investigate methods for verifying users with a short randomized pass-phrase due to the increasing demand for voice-based authentication systems. In this paper, we propose a novel technique for extracting an i-vector-like feature based on an adversarially learned inference (ALI) model which summarizes the variability within the Gaussian mixture model (GMM) distribution through a nonlinear process. Analogous to the previously proposed variational autoencoder (VAE)-based feature extractor, the proposed ALI-based model is trained to generate the GMM supervector according to the maximum likelihood criterion given the Baum–Welch statistics of the input utterance. However, to prevent the potential loss of information caused by the Kullback–Leibler divergence (KL divergence) regularization adopted in the VAE-based model training, the newly proposed ALI-based feature extractor exploits a joint discriminator to ensure that the generated latent variable and the GMM supervector are more realistic. The proposed framework is compared with the conventional i-vector and VAE-based methods using the TIDIGITS dataset. Experimental results show that the proposed method can represent the uncertainty caused by the short duration better than the VAE-based method. Furthermore, the proposed approach has shown great performance when applied in association with the standard i-vector framework.

Facile realization of Ag functionalized W18O49@PPy core–shell nanorods for multieffect modulation on gas sensing response

Surface modification and core–shell heterostructure are all proved effective in improving gas-response performance of a semiconductor sensor. Here Ag nanoparticles (Ag NPs) modified W18O49@PPy core–shell nanorods (Ag/CSNRs) prepared by a cost-effective process are investigated for enhanced NH3-sensing response. In present process, surface modification by Ag NPs is achieved in parallel to the PPy-shell formation via chemical polymerization of Py on W18O49 nanorods with AgNO3 as an oxidant. The distribution (partial or total embedding in PPy-shell) and amount of the modified Ag NPs are revealed to be decided by Py polymerization duration, and affect the gas response of corresponding Ag/CSNRs sensor. The sensor based on Ag/CSNRs exhibits significantly enhanced response. The sample modified with partially embedded Ag NPs is capable of detection to rarefied NH3 as low as 100 ppb at room temperature. However, long duration of polymerization induces a full coverage of Ag NPs by PPy film and thus seriously weakens the modification effect to gas response. A sensing mechanism model was proposed and the response feature of Ag/CSNRs was analyzed with the perspective of co-contribution of chemical sensitization and electronic sensitization, which is further correlated to the distribution of attached Ag NPs.

Unsupervised Learning of Total Variability Embedding for Speaker Verification with Random Digit Strings

Recently, the increasing demand for voice-based authentication systems has encouraged researchers to investigate methods for verifying users with short randomized pass-phrases with constrained vocabulary. The conventional i-vector framework, which has been proven to be a state-of-the-art utterance-level feature extraction technique for speaker verification, is not considered to be an optimal method for this task since it is known to suffer from severe performance degradation when dealing with short-duration speech utterances. More recent approaches that implement deep-learning techniques for embedding the speaker variability in a non-linear fashion have shown impressive performance in various speaker verification tasks. However, since most of these techniques are trained in a supervised manner, which requires speaker labels for the training data, it is difficult to use them when a scarce amount of labeled data is available for training. In this paper, we propose a novel technique for extracting an i-vector-like feature based on the variational autoencoder (VAE), which is trained in an unsupervised manner to obtain a latent variable representing the variability within a Gaussian mixture model (GMM) distribution. The proposed framework is compared with the conventional i-vector method using the TIDIGITS dataset. Experimental results showed that the proposed method could cope with the performance deterioration caused by the short duration. Furthermore, the performance of the proposed approach improved significantly when applied in conjunction with the conventional i-vector framework.

Column generation algorithms for virtual network embedding in flexi-grid optical networks.

Network virtualization provides means for efficient management of network resources by embedding multiple virtual networks (VNs) to share efficiently the same substrate network. Such virtual network embedding (VNE) gives rise to a challenging problem of how to optimize resource allocation to VNs and to guarantee their performance requirements. In this paper, we provide VNE algorithms for efficient management of flexi-grid optical networks. We provide an exact algorithm aiming to minimize the total embedding cost in terms of spectrum cost and computation cost for a single VN request. Then, to achieve scalability, we also develop a heuristic algorithm for the same problem. We apply these two algorithms for a dynamic traffic scenario where many VN requests arrive one-by-one. We first demonstrate by simulations for the case of a six-node network that the heuristic algorithm obtains very close blocking probabilities to exact algorithm (about 0.2% higher). Then, for a network of realistic size (namely, USnet) we demonstrate that the blocking probability of our new heuristic algorithm is about one magnitude lower than a simpler heuristic algorithm, which was a component of an earlier published algorithm.

Collaborative Dynamic Virtual Network Embedding Algorithm Based on Resource Importance Measures

Many existing virtual network embedding (VNE) algorithms adopt the greedy embedding strategies, which embed the in-progress virtual network requests (VNRs) onto the substrate nodes and links with more residual resources. However, the previous VNRs may overconsume the critical substrate resources and lead to the resource fragmentation problem in the substrate networks, thus reducing the acceptance ratio of the latter VNRs. To address this problem, we propose a novel collaborative VNE algorithm based on resource importance measures, RIM-ViNE, in which the global node importance and link importance in the initial substrate network are measured using multiple topological attributes and are used to set the embedding cost of VNRs. Then, the VNE problem is defined as a linear programming model and is solved by minimizing the total embedding cost, which could bring about the coordinative embedding between different VNRs, and thus preventing the critical resources from being over occupied by the previous VNRs with small requirements and improving the resource fragmentation problem. Moreover, we propose a dynamic reconfiguration mechanism based on critical nodes protection (CNP-Re) to improve the resource fragmentation problem further. Extensive experiments are conducted under two network scenarios, and the results show that the proposed algorithms outperform the VNE algorithms that only consider the coordination between node mapping and link mapping or only measure the node importance in the residual substrate network, and the average VNR acceptance ratio, average revenue, and average resource utilization ratio are effectively improved.

Fano 3-folds in format, Tom and Jerry

We study {mathbb {Q}}-factorial terminal Fano 3-folds whose equations are modelled on those of the Segre embedding of . These lie in codimension 4 in their total anticanonical embedding and have Picard rank 2. They fit into the current state of classification in three different ways. Some families arise as unprojections of degenerations of complete intersections, where the generic unprojection is a known prime Fano 3-fold in codimension 3; these are new, and an analysis of their Gorenstein projections reveals yet other new families. Others represent the “second Tom” unprojection families already known in codimension 4, and we show that every such family contains one of our models. Yet others have no easy Gorenstein projection analysis at all, so prove the existence of Fano components on their Hilbert scheme.

Synchronous, bilateral tonsillar carcinomas: Patient characteristics and human papillomavirus genotypes

IntroductionThe incidence of oropharyngeal squamous cell carcinoma (OPSCC) is increasing, but data on the incidence of synchronous, bilateral tonsillar squamous cell carcinomas (BiTSCCs) is sparse. In this study, we report the incidence and tumour characteristics of BiTSCCs in a population-based, consecutive cohort of OPSCCs. MethodsWe identified all patients diagnosed with tonsillar squamous cell carcinoma (TSCC) in eastern Denmark during a 15-year period to detect the incidence of synchronous BiTSCCs. The tumours were assessed for p16Ink4a expression, the presence of HPV DNA and HPV genotypes. Furthermore, we systematically reviewed the literature examining BiTSCCs. ResultsOf the total of 1119 TSCCs diagnosed in eastern Denmark from 2000 to 2014, we identified 12 BiTSCCs, nine of which initially presented as a cancer of unknown primary (CUP) in the neck. Nine cases were bilaterally HPV16 positive (HPV16+), while two cases were HPV16+ in one tonsil and respectively, HPV33 and HPV35 positive in the contralateral tonsil. One case was bilaterally HPV-negative. We also identified an increase in the incidence of BiTSCCs after 2012 when histological examination of the entire tonsil tissue became routine, suggesting that BiTSCCs might be underdiagnosed. In the literature, we identified 15 studies from six countries, encompassing 25 cases in total. ConclusionsBiTSCCs were primarily HPV16+ and were most often diagnosed as part of the diagnostic work-up for CUP. We found an incidence of 9% BiTSCCs in patients with TSCC after 2012 and we therefore recommend focusing on putative BiTSCC with total embedding and histological examination of tonsils harvested by bilateral tonsillectomies.

Out-of-Sample Generalizations for Supervised Manifold Learning for Classification.

Supervised manifold learning methods for data classification map high-dimensional data samples to a lower dimensional domain in a structure-preserving way while increasing the separation between different classes. Most manifold learning methods compute the embedding only of the initially available data; however, the generalization of the embedding to novel points, i.e., the out-of-sample extension problem, becomes especially important in classification applications. In this paper, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with an iterative process. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

A Distortion-Free Data Hiding Scheme for Triangular Meshes Based on Recursive Subdivision

This study adopts a triangle subdivision scheme to achieve reversible data embedding. The secret message is embedded into the newly added vertices. The topology of added vertex is constructed by connecting it with the vertices of located triangle. For further raising the total embedding capacity, a recursive subdivision mechanism, terminated by a given criterion, is employed. Finally, a principal component analysis can make the stego model against similarity transformation and vertex/triangle reordering attacks. Our proposed algorithm can provide a high and adjustable embedding capacity with reversibility. The experimental results demonstrate the feasibility of our proposed algorithm.

A Comparison of Prostatic Acinar Adenocarcinoma Gleason 3+4 versus 4+3 in Different Laboratory Practice Settings

Objective: To evaluate Gleason 7 tumors from radical prostatectomy specimens that were stratified by 3+4 and 4+3 status, focusing on the clinical and morphologic features. Methods and results: In the first series of 20 Gleason 7 tumors processed by total embedding, cases with 4+3 status were associated with a higher proliferation index. In a second series of 106 Gleason 7 tumors processed by partial embedding, 4+3 status was associated with higher preoperative serum prostate-specific antigen levels and a higher frequency of extraprostatic extension and seminal vesicle invasion. Conclusion: For those laboratories that employ partial samplings as the adopted method for processing radical prostatectomy specimens, inclusion of the entire prostatic periphery and extraprostatic tissue is advised to avoid understaging caused by missing extraprostatic extension.