Reviewerly/exHarmony huggingface dataset:
Parameterized codes over graphs

In this article we review known results on parameterized linear codes over graphs, introduced by Rentería et al. (Finite Fields Appl 17(1):81–104, 2011) . Very little is known about their basic parameters and invariants. We review in detail the parameters dimension, regularity and minimum distance. As regards the parameter dimension, we explore the connection to Eulerian ideals in the ternary case and we give new combinatorial formulas.
Adaptive weight multi-channel center similar deep hashing

To increase the richness of the extracted text modality feature information and deeply explore the semantic similarity between the modalities. In this paper, we propose a novel method, named adaptive weight multi-channel center similar deep hashing (AMCDH). The algorithm first utilizes three channels with different configurations to extract feature information from the text modality; and then adds them according to the learned weight ratio to increase the richness of the information. We also introduce the Jaccard coefficient to measure the semantic similarity level between modalities from 0 to 1, and utilize it as the penalty coefficient of the cross-entropy loss function to increase its role in backpropagation. Besides, we propose a method of constructing center similarity, which makes the hash codes of similar data pairs close to the same center point, and dissimilar data pairs are scattered at different center points to generate high-quality hash codes. Extensive experimental evaluations on four benchmark datasets show that the performance of our proposed model AMCDH is significantly better than other competing baselines. The code can be obtained from https://github.com/DaveLiu6/AMCDH.git.
Stereo image quality assessment considering the difference of statistical feature in early visual pathway

Human visual theory is closely related to stereo image quality assessment (SIQA), which determines whether the evaluation results of SIQA method can keep good consistency with subjective perception. Many SIQA methods are not fully based on human visual theory, so there is still room for improvement. The research on the visual system tends to the dorsal and ventral pathways, which ignores the information differences in the early visual pathways. It is worth noting that the ON and OFF receptive fields in retinal ganglion cells (RGCs) respond asymmetrically to the statistical features of images. Inspired by this, in this paper, we propose an SIQA method based on monocular and binocular visual features, which takes into account the difference of ON and OFF response features in early visual pathways. Moreover, the different information interaction mechanisms of visual cortex are used to fuse the response maps information of left and right images. Final, monocular and binocular features are extracted and sent to support vector regression (SVR) for quality prediction. Experimental results show that the proposed method is superior to several mainstream SIQA metrics on four publicly available stereo image databases.
Crypt-OR: A privacy-preserving distributed cloud computing framework for object-removal in the encrypted images

Image inpainting is a technique to modify an image by removing/fill-up the undesired region(s) in a visually plausible manner. With the advancement of cloud applications, the cloud service providers (CSPs) provide image modifying services such as inpainting and undesired object removal to their users. However, these inpainting services require the user’s original image containing sensitive information that an adversary can misuse. In this paper, we address these privacy concerns in image enhancement techniques by proposing a novel privacy-preserving distributed-cloud-based framework for object removal in the encrypted images, namely Crypt-OR. The unknown pixels intensity value, obtained by removing the specified object(S), is approximated by incorporating the merits of the exemplar-based search–copy–paste approach and diffusion equation on the Shamir’s secret shares. The qualitative and quantitative analysis of Crypt-OR is evaluated over different real-world images with varying shaped and sized objects in the encrypted domain (ED). Crypt-OR outperforms the traditional exemplar-based object-removal schemes and is comparable with generative network-based inpainting schemes in the plain domain (PD). Further, Crypt-OR is proved information-theoretically secure in probabilistic and entropy viewpoints with standard cryptographic adversary attacks.
Place and Role of the Discipline ‘Foreign Language’ in the Formation and Development of Human Capital at University

Purpose This paper aims at determining the place and role of the course ‘Foreign Language’ in the formation and development of human capital at university. Human capital has become the key factor in modern society. In this paper, we define human capital, its description and analysis. The level of human capital development can be evaluated with the Human Development Index taking into account life expectancy, education level, the standard of living and others. Design/methodology/approach Interdisciplinary approach is chosen and defined as one of the most effective means to increase the Human Development Index for Russia. Findings It is stated that universities are to take the leading role and apply an interdisciplinary approach to university education and work out some tools for the student’s professional development. Such an approach will contribute to their successful socialization in the professional community. The academic discipline ‘Foreign Language’ at university is a means of professional communicative competence formation. It enables specialists to be more competitive in the international labour market and more productive in the workplace. Communicative competence is achieved by working with specially designed course books, using such methods as content-language integrated learning—CLIL, dealing with tasks aimed at developing soft skills. Foreign languages in the modern educational process are at the forefront of the student’s professional growth, they retain a leading position in the development of human capital in the context of digitalization of the world community and the use of online learning opportunities. Originality/value Interdisciplinary approach is applied while designing the forms and tasks for miscellaneous ESP courses developed at the Foreign Languages Departments both in Moscow Institute of Physics and Technology and Southern Federal University. The classification of tasks for soft skills and, consequently, human capital development is presented in the article.
Digital Tools for the Export of Educational Services in the Post-soviet Space

Purpose The purpose of the study is a comprehensive analysis of the main socio-economic mechanisms for ensuring the export of educational services to the world market, substantiation of a sustainable mechanism for the interaction of participants in the educational process in a digital environment based on the active use of information and communication technologies in the context of human capital development. Methodology The study uses a collection of empirical data reflecting the composition and dynamics of exports of Russian educational services. Based on analytical methods, an algorithm of digital interaction of participants in the educational process has been developed. Findings The considered problem of the study is the substantiation of approaches to the export-oriented model of Russian education with an analysis of the main socio-economic, information and communication tools for achieving the objectives. The relevance of scientific research is determined by the need to increase the competitiveness of Russian educational products on the world market in the context of digital transformation of the educational environment based on the development of remote access technologies. The factors influencing the competitive advantages of Russian educational products in the context of human capital development at the micro- and meso-economic levels are established. Originality The authors substantiate the tools for using the identified competitive advantages that allow the formation of high-tech educational clusters that contribute to the diversification of the Russian economy.
Transformation of the Higher Education Services Market: Comparative Analysis by OECD Countries

The research hypothesis consists in the scientific assumption that in modern conditions there is a transformation of the market of higher education services in terms of increasing openness, globality and expanding online accessibility due to the raised dynamics of society and technology development, as well as under the influence of the COVID-19 pandemic. The research is aimed at conducting a comparative analysis of the higher education services market in the OECD countries, as well as identifying its transformational trends and determining the directions of state educational policy that contribute to adaptation to new requirements. The authors identified transformational trends in the market of higher education: an increase in the educational level of the population and the demand for higher education services; increased dynamics of the development of society and technology, requiring regular updates and additions to basic higher education; expansion of requirements for educational services in terms of content, volume, forms and methods of obtaining knowledge following changing needs of society. It is determined that during the pandemic, the higher education system of the OECD countries faced challenges related to the organization of remote online exams; unequal technological equipment of participants in the learning process; risks of technical failure; lack of effective forms of e-learning for the training of specialists of a narrow profile. The state policy measures proposed by the authors can be used to overcome the difficulties of the transformational and pandemic period in the higher education of the OECD countries.
The Introduction of a Value-Oriented Approach to the Management of Modern Universities in the Formation of Human Capital in an Innovative Economy

Modern universities have a significant impact on the formation of human capital. Human capital, on the one hand, is a reflection of accumulated knowledge, social and personality attributes, labor skills that are necessary for participation in social and economic activities, on the other hand, it is the result of continuous self-development and vocational training. The dynamic development of society and the economy puts forward new requirements for knowledge, skills and abilities, which gives a powerful impetus to the development of universities themselves. The introduction of a value-oriented approach to the management of modern universities makes it possible to establish a new holistic university environment aimed at the formation of intellectual abilities and practical skills in the innovative economy.
Ontology Mapping for Personalization in Adaptive E-learning

Ontology mapping is critical in personalized learning for ontology-based information integration, e-learning systems interoperability, resources searching, recommendation and reuse. In this work we analyze and discuss ontology mapping methods, techniques strategies and its applicability and usefulness in e-learning domain. We also outline trends and discuss problems, related to ontology mapping in e-learning context.
Privacy-Preserving Task Distribution Mechanism with Cloud-Edge IoT for the Mobile Crowdsensing

Mobile crowdsensing under big data provides an efficient, win-win, and low-budget data collection solution for IoT applications such as the smart city. However, its open and all access scenarios raise the threat of data security and user privacy during task distribution of mobile crowdsensing. To eliminate the above threat, this paper first designs a privacy-preserving task distribution scheme (Scheme 1), which realizes fine-grained access control and the practical keyword search, as well as protects the access policy. But it incurs expensive computational and communication consumptions for the task performer side. In this regard, we construct Scheme 2 to attain a lightweight trapdoor generation and keyword search mechanism, and it enables the crowdsensing platform to predecrypt a ciphertext without revealing any information about the task and the performer’s privacy. Then, the resource-constrained device on the task performer side can recover the task with a few computational and communication overheads. The security of the scheme has been detailedly proved and analyzed, and theoretical comparisons and experiment demonstrate their practicability.
Algorithmization of the Process of Recognition of Biological Objects by Computed Tomography

This paper studies the results from the development of a model of a system for disease diagnosis based on an analytical method by image segmentation. Image segmentation has shown excellent efficiency for processing images acquired by computed tomography. The proposed model of a system for diagnosis has substantially enlarged the possibilities for an integrated approach when solving problems and discovering pathologies. This model has been tested on a computed tomography image – human brain with lesion.
A Novel Human Image Sequence Synthesis Method by Pose-Shape-Content Inference

In online clothing sales, static model images only describe specific clothing statuses towards consumers. Without increasing shooting costs, it is a subject to display clothing dynamically by synthesizing a continuous image sequence between static images. This paper proposes a novel human image sequence synthesis method by pose-shape-content inference. In the condition of two reference poses, the pose is interpolated in the pose manifold controlled by a linear parameter. The interpolated pose is transferred into the end shape by AdaIN and the attention mechanism to infer target shape. Then the content in the reference image is transferred into this target shape. In the content transfer, the visual features of the human body cluster and clothing cluster are extracted, respectively. And the Sobel gradient is adopted to extract clothing texture variation. In the feature inferring, the multiscale feature-level optical flow warps source features, and style code infusion infers new region content without source features. Extensive experiments demonstrate that our method is superior in inferring clear layouts and transferring reasonable content compared to the pose transfer baselines. Moreover, our method has been verified to apply in parsing-guided image inference and dynamic display based on the pose sequence.
Intrinsically Stable 2-GHz Polarization Modulation for Satellite-Based Quantum Key Distribution

Satellite-based quantum key distribution (QKD) has made significant strides in the past few years, and an international space race toward practical quantum networks has begun. Being one major encoding method, highly robust, high-performance and miniaturized polarization modulation is critical to meet the needs of future satellite applications. Here, we employ the Sagnac-based method to realize inherent-stable polarization modulation and propose a novel optical design to achieve a state-of-the-art repetition rate of 2 GHz. This value approaches the theoretical repetition frequency limit with commercially-available phase modulators. Furthermore, the polarization encoder is miniaturized into a robust 6.0 cm × 6.5 cm module and two commercially mature phase modulators. A 90-min consecutive test was successfully performed, verifying the robustness of this scheme with an average quantum bit error rate as low as 0.42%. The presented intrinsically stable high-speed polarization modulation provides an effective solution for future satellite applications.
Fast Recognition of Snap-Fit for Industrial Robot Using a Recurrent Neural Network

Snap-fit recognition is an essential capability for industrial robots in manufacturing. The goal is to protect fragile parts by quickly detecting snap-fit signals in the assembly. In this letter, we propose a fast recognition method of snap-fit for industrial robots. A snap-fit dataset generation strategy of automatically acquiring labels is presented in the presence of data collection is complicated. A multilayer recurrent neural network (RNN) is designed for snap-fit recognition. An extensive evaluation based on two different datasets shows that the proposed method makes reliable and fast recognitions. Real-time experiments on industrial robot also demonstrate the effectiveness of the proposed method.
A General Method for Synthesis of Constant-Amplitude Ideal Periodic Autocorrelation Phase Manipulated Signals

Due to their wide application, the constant-amplitude ideal periodic autocorrelation phase manipulated signals (CAIPAPMSs) have been actively explored during the past seventy years. As a result, a large set of methods for synthesis of CAIPAMSs has been proposed and practically adopted. Accounting this situation in the paper a general method for synthesis of CAIPAPMSs is substantiated.
A Methodology for Mapping Educational Domain Ontologies Using Top Level Ontologies

A grand variety of ontology mapping methods algorithms and tools have been proposed recently, but no one can guarantee the automatically-generated alignment’s correctness and completeness. In this paper we discuss usefulness of the usage of ontologies and thesauruses as background knowledge for ontology mapping and propose a methodology for educational ontology mapping using such background knowledge.
GRay: Ray Casting for Visualization and Interactive Data Exploration of Gaussian Mixture Models

The Gaussian mixture model (GMM) describes the distribution of random variables from several different populations. GMMs have widespread applications in probability theory, statistics, machine learning for unsupervised cluster analysis and topic modeling, as well as in deep learning pipelines. So far, few efforts have been made to explore the underlying point distribution in combination with the GMMs, in particular when the data becomes high-dimensional and when the GMMs are composed of many Gaussians. We present an analysis tool comprising various GPU-based visualization techniques to explore such complex GMMs. To facilitate the exploration of high-dimensional data, we provide a novel navigation system to analyze the underlying data. Instead of projecting the data to 2D, we utilize interactive 3D views to better support users in understanding the spatial arrangements of the Gaussian distributions. The interactive system is composed of two parts: (1) raycasting-based views that visualize cluster memberships, spatial arrangements, and support the discovery of new modes. (2) overview visualizations that enable the comparison of Gaussians with each other, as well as small multiples of different choices of basis vectors. Users are supported in their exploration with customization tools and smooth camera navigations. Our tool was developed and assessed by five domain experts, and its usefulness was evaluated with 23 participants. To demonstrate the effectiveness, we identify interesting features in several data sets.
Convolved Quality Transformer: Image Quality Assessment via Long-Range Interaction Between Local Perception

A hybrid architecture composed of a convolutional neural network (CNN) and a Transformer is the new trend in realizing various vision tasks while pushing the limits of learning representation.From the perspective of mechanisms of CNN and Transformer, a functional combination of them is suitable for the image quality assessment (IQA) since which requires leveraging both local distortion perception and global quality aggregation, however, there has been scarce study employing such an approach.This paper presents an end-to-end CNN-Transformer hybrid model for full-reference IQA named convolved quality transformer (CQT).The CQT is inspired by the human's perceptual characteristics and is designed to unify the advantages of both CNN and Transformer for evaluating quality score.In CQT, convolutional layers specialize in local distortion feature extraction whereas Transformer aggregates them to estimate holistic quality via long-range interaction between them.Such a series of processes is repeated on multiscale feature maps to capture quality representation sensitively.To verify submodules in CQT perform their roles properly, we in-depth analyze the interaction between local distortions inferring global quality with attention visualization.Finally, the perceptually pooled information from stage-wise feature embeddings derives the final quality level.The experimental results demonstrate that the proposed model achieves superior performance in comparison to previous data-driven approaches, and which is even well-generalized over standard datasets.
Domain Generalization via Adversarially Learned Novel Domains

This study focuses on the domain generalization task, which aims to learn a model that generalizes to unseen domains by utilizing multiple training domains.More specifically, we follow the idea of adversarial data augmentation, which aims to synthesize and augment training data with "hard" domains to improve the model's domain generalization ability.However, previous studies augmented training data only with samples similar to the training data, resulting in limited generalization ability.To alleviate this issue, we propose a novel adversarial data augmentation method, termed GADA (generative adversarial domain augmentation), which employs an image-to-image translation model to obtain a distribution of novel domains that are semantically different from the training domains, and, at the same time, hard to classify.Evaluation and further analysis suggest that GADA fits our expectation; adversarial data augmentation with semantically different samples leads to better domain generalization performance.