Reviewerly/exHarmony huggingface dataset:
Proposals for university promoting in QS rating based on the methods of statistical analysis

The purpose of the study . The purpose of the study is to develop scientifically based proposals to increase the university performance indicators in the international institutional rating QS to the required values, taking into account the presence of a combination of latent (hidden) factors, the degree of achievement of the set values of the basic indicators and, as a result, the university ranking level. Materials and methods . To achieve this goal, methods of statistical analysis (correlation-regression and factor analysis) were used, which made it possible to identify the degree of influence of latent factors on basic indicators and the main indicator (rating functional). During the study, the following tasks were solved: identification of latent factors affecting the basic indicators of the university, an assessment of their significance and degree of influence on the basic indicators, as well as their grouping. Based on the results of the correlation - regression and factor analysis, measures are formulated to achieve the specified values of the QS University institutional rating indicators. Results . An approach to solving the problem of providing conditions for achieving the required values of university performance indicators in the international institutional ranking QS using models developed based on the methods of correlation-regression and factor analysis is proposed. Estimates of the relationship of indicators and university ranking based on the methods of correlation and regression analysis are obtained. A comparative analysis of the results obtained at the universities of the reference group is made. The problem of identifying factors that influence the change in the values of indicators is solved; the degree of this influence is assessed. Based on the results obtained, reasonable proposals have been developed to achieve the required values of the basic indicators and the rating functional of the university. Conclusion . The results obtained in the course of the study made it possible to justify the measures necessary to solve the problem of achieving the specified performance indicators of the university. Based on the correlation model, correlation dependencies between the rating functional and basic indicators are obtained. Interpretation of the results of factor analysis allowed us to identify a set of factors that have a significant impact on the basic indicators. It is shown that measures to achieve the specified indicators must be carried out taking into account the revealed correlation dependencies between factors and basic indicators, as well as the interpretation results of the developed factor model.
Specific Features of Educational and Pedagogical Discourse in the Context of Anthropological Challenges: Socio-Cultural Approach

The relevance of the research is due to the destructive influence of digitalization of society, globalization processes, technological and social progress on a person in his spiritual and physical integrity. These are, first of all, such technological and sociocultural factors as virtualization of the individual’s consciousness, cyborgization, loss of subjectivity in the process of merging with information machine complexes, genetic engineering, cloning, pharmaceutical modifying human influence. It is proved that a broad interpretation of educational and pedagogical discourse is adequate for the modern socio-cultural space, which is becoming more complicated for description, which makes it synonymous with such a basic category for pedagogical science as the pedagogical process. The marked tendency is estimated as negative, as the categorical value of basic concepts for pedagogy is lost, the subject of pedagogical science in the system of human Sciences is blurred. Therefore, a highly specialized definition of specific features of pedagogical discourse is justified. It is concluded that the pedagogical category “zone of immediate development” in its socio-cultural hypostasis contributes to the designation of specific features of educational and pedagogical discourse, focusing on specific pedagogical tasks. For example orientation to the zone of the nearest development of the individual in the process of his constant development and self-development assumes permanent diagnostic correction on the part of the teacher in relation to the student. This favors the consistency of the socio-cultural experience transmitted by the teacher with the life experience of the student, the emotional “inclusion” of the latter in the educational process
A hybrid swarm intelligence with improved ring topology for nonlinear equations

Solving nonlinear equations (NEs) is one of the most important yet challenging tasks in numerical computation, especially for the simultaneous location of multiple roots in a single run. In this paper, a hybrid swarm intelligence approach with improved ring topology is proposed to tackle this problem. It has three main features: (i) the improved ring topology is developed to effectively use the neighborhood knowledge; (ii) the hybrid swarm intelligence enhances the search efficiency; and (iii) an individual reinitialization mechanism is proposed to enrich the population diversity. The performance of this approach is tested on eight NE problems with multiple roots, experimentally confirming that it can simultaneously locate multiple roots in a single run. In addition, it can provide better results than conventional methods in terms of both root and success rates.
Background subtraction using infinite asymmetric Gaussian mixture models with simultaneous feature selection

Mixture models are broadly applied in image processing domains. Related existing challenges include failure to approximate exact data shapes, estimate correct number of components, and ignore irrelevant features. In this study, the authors develop a statistical self-refinement framework for the background subtraction task by using Dirichlet Process-based asymmetric Gaussian mixture model. The parameters of this model are learned using variational inference methods. They also incorporate feature selection simultaneously within the framework to avoid noisy influence from uninformative features. To validate the proposed framework, they report their results on background subtraction tasks on 8 different datasets for infrared and visible videos.
Steganographer Detection via Multi-Scale Embedding Probability Estimation

Steganographer detection aims to identify the guilty user who utilizes steganographic methods to hide secret information in the spread of multimedia data, especially image data, from a large amount of innocent users on social networks. A true embedding probability map illustrates the probability distribution of embedding secret information in the corresponding images by specific steganographic methods and settings, which has been successfully used as the guidance for content-adaptive steganographic and steganalytic methods. Unfortunately, in real-world situation, the detailed steganographic settings adopted by the guilty user cannot be known in advance. It thus becomes necessary to propose an automatic embedding probability estimation method. In this article, we propose a novel content-adaptive steganographer detection method via embedding probability estimation. The embedding probability estimation is first formulated as a learning-based saliency detection problem and the multi-scale estimated map is then integrated into the CNN to extract steganalytic features. Finally, the guilty user is detected via an efficient Gaussian vote method with the extracted steganalytic features. The experimental results prove that the proposed method is superior to the state-of-the-art methods in both spatial and frequency domains.
Deep learning based searching approach for RDF graphs

The Internet is a remarkably complex technical system. Its rapid growth has also brought technical issues such as problems to information retrieval. Search engines retrieve requested information based on the provided keywords. Consequently, it is difficult to accurately find the required information without understanding the syntax and semantics of the content. Multiple approaches are proposed to resolve this problem by employing the semantic web and linked data techniques. Such approaches serialize the content using the Resource Description Framework (RDF) and execute the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although, it improves the keyword-based search; however, it does not provide probabilistic reasoning to find the semantic relationship between the queries and their results. From this perspective, in this paper, we propose a deep learning-based approach for searching RDF graphs. The proposed approach treats document requests as a classification problem. First, we preprocess the RDF graphs to convert them into N-Triples format. Second, bag-of-words (BOW) and word2vec feature modeling techniques are combined for a novel deep representation of RDF graphs. The attention mechanism enables the proposed approach to understand the semantic between RDF graphs. Third, we train a convolutional neural network for the accurate retrieval of RDF graphs using the deep representation. We employ 10-fold cross-validation to evaluate the proposed approach. The results show that the proposed approach is accurate and surpasses the state-of-the-art. The average accuracy, precision, recall, and f-measure are up to 97.12%, 98.17%, 95.56%, and 96.85%, respectively.
Ellipsoidal Subspace Support Vector Data Description

In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification.The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data.We provide both linear and non-linear formulations for the proposed method.The method takes into account the covariance of the data in the subspace; hence, it yields a more generalized solution as compared to the data description in the subspace by hyperspherical encapsulation of target class data.We propose different regularization terms expressing the class variance in the projected space.We compare the results with classic and recently proposed one-class classification methods and achieve competing results and show clear improvement compared to the other support vector based methods.The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.
Strong-coupling quantum logic of trapped ions

Essentially all known quantum gates rely on a weak-coupling approximation resulting in linear dynamics. With the explicit example of trapped ions, we show how high-fidelity quantum gates can be achieved outside such an approximation, and we derive readily implementable driving fields to realize gates with extremely high fidelities for ions well outside the Lamb-Dicke regime with motional temperatures achievable by only Doppler cooling.
Parallelized Multiple Swarm Artificial Bee Colony (PMS-ABC) Algorithm for Constrained Optimization Problems

Since their introduction, bio-inspired algorithms, especially the ones based on the social behaviour of the animals that live in colonies have demonstrated great potential in finding near-optimal solutions for both unconstrained and constrained hard optimization problems.In this research, a parallel version of the popular Artificial Bee Colony (ABC) algorithm for optimization of constrained problems, has been introduced.An island-based model, in which the whole population is divided into subpopulations, is used.Subpopulations execute the serial version of the original algorithm and occasionally exchange the obtained results.The proposed algorithm has been tested based on a set of well-known constraint benchmark functions and five real-world engineering design problems.The results demonstrate clear improvements compared with those obtained with the original ABC algorithm.
Modular Rover Design for Exploration and Analytical Tasks

Rovers and other unmanned ground vehicles (UGV) were a matter of space exploration or post-disaster reconnaissance. Rovers work in tough conditions where presence of a human is uncomfortable or dangerous. This results in main requirements for rover - durable chassis, stable communication for direct control or transfer of data and task-specific tools for experiments and measurements. In this article we propose solutions for fulfilling those requirements.
Improving spam email detection using hybrid feature selection and sequential minimal optimisation

&lt;p&gt;Communication by email is counted as a popular manner through which users can exchange information. The email could be abused by spammers to spread suspicious content to the Internet users. Thus, the need to an effective way to detect spam emails are becoming clear to keep this information safe from malicious access. Many methods have been developed to address such a problem. In this paper, a machine learning technique is applied to detect spam emails. In this technique, a detection system based on sequential minimal optimization (SMO) is built to classify emails into two categories: spam and non-spam (ham). Each email is represented by a set of features extracted from its textual content. A hybrid feature selection is developed to choose a subset of these features based on their importance in process of the detection. This subset is then input into the SMO algorithm to make the detection decision. The use of such a technique provides an efficient protective mechanism to control spams. The experimental results show that the performance of the proposed method is promising compared with the existing methods.&lt;/p&gt;
Camera–projector system calibration method based on optimal polarization angle

In the structured light three-dimensional measurement system, calibration is crucial for measurement accuracy. However, conventional projector calibration methods involve complicated procedures. Therefore, a novel method is developed for projector calibration. The key concept is to establish the relationship between projector coordinates and the world coordinates using the projection checkerboard, which enables the calibration of the projector to be the same as that of a camera. In addition, the edges of the checkerboard form different degrees of high-light regions when the projection checkerboard is collected. These high-light regions affect the extraction of corner points and cause low accuracy of the projector calibration. Therefore, the projection checkerboard is collected by a CCD camera equipped with a polarizer at the optimal polarization angle. Then the subpixel corner extraction algorithm based on the homography matrix mapping is used to extract the subpixel coordinates of the corners. Finally, the projector can be calibrated with the same method as the camera. In the experimental part, the validity of the method is verified by the reprojection system.
MORPHOLOGICAL ANALYSIS OF CELLS BY MEANS OF AN ELASTIC METRIC IN THE SHAPE SPACE

Shape analysis is of great importance in many fields, such as computer vision, medical imaging, and computational biology. This analysis can be performed considering shapes as closed planar curves in the shape space. This approach has been used for the first time to obtain the morphological classification of erythrocytes in digital images of sickle cell disease considering the shape space S1, which has the property of being isometric to an infinite-dimensional Grassmann manifold of two-dimensional subspaces (Younes et al., 2008), without taking advantage of all the features offered by the elastic metric related to the possibility of stretching and bending of the curves. In this paper, we study this deformation in the shape space, S2, which is based on the representation of closed planar curves by means of the square-root velocity function (SRVF) (Srivastava et al., 2011), using the elastic metric of this space to obtain more efficient geodesics and geodesic lengths between planar curves. Supervised classification with this approach achieved an accuracy of 94.3%, classification using templates achieved 94.2% and unsupervised clustering in three groups achieved 94.7%, considering three classes of erythrocytes: normal, sickle, and with other deformations. These results are better than those previously achieved in the morphological analysis of erythrocytes and the method can be used in different applications related to the treatment of sickle cell disease, even in cases where it is necessary to study the process of evolution of the deformation, something that can not be done in a natural way in the feature space.
Improvements on the virtual obstacle method

While the artificial potential field has been widely employed to design path planning algorithms, it is well-known that artificial potential field-based algorithms suffer a severe problem that a robot may sink into a local minimum point. To address such problems, a virtual obstacle method has been developed in the literature. However, a robot may be blocked by virtual obstacles generated during performing the virtual obstacle method if the environments are complex. In this article, an improved virtual obstacle method for local path planning is designed via proposing a new minimum criterion, a new switching condition, and a new exploration force. All the three new contributions allow to overcome the drawbacks of the artificial potential field-based algorithms and the virtual obstacle method. As a consequence, feasible collision-free paths can be found in complex environments, as illustrated by final numerical simulations.
Weak Keys in the Rekeying Paradigm: Application to COMET and mixFeed

In this paper, we study a group of AEAD schemes that use rekeying as a technique to increase efficiency by reducing the state size of the algorithm. We provide a unified model to study the behavior of the keys used in these schemes, called Rekey-and-Chain (RaC). This model helps understand the design of several AEAD schemes. We show generic attacks on these schemes based on the existence of certain types of weak keys. We also show that the borderline between multi-key and single-key analyses of these schemes is not solid and the analysis can be performed independent of the master key, leading sometimes to practical attacks in the multi-key setting. More importantly, the multi-key analysis can be applied in the single key setting, since each message is encrypted with a different key. Consequently, we show gaps in the security analysis of COMET and mixFeed in the single key setting, which led the designers to provide overly optimistic security claims. In the case of COMET, full key recovery can be performed with 264 online queries and 264 offline queries in the single-key setting, or 246 online queries per user and 264 offline queries in the multi-key setting with ∼ 0.5 million users. In the case of mixFeed, we enhance the forgery adversarial advantage in the single-key setting with a factor of 267 compared to what the designers claim. More importantly, our result is just a lower bound of this advantage, since we show that the gap in the analysis of mixFeed depends on properties of the AES Key Schedule that are not well understood and require more cryptanalytic efforts to find a more tight advantage. After reporting these findings, the designers updated their security analyses and accommodated the proposed attacks.
Mitigating Adversarial Attacks for Deep Neural Networks by Input Deformation and Augmentation

Typical Deep Neural Networks (DNN) are susceptible to adversarial attacks that add malicious perturbations to input to mislead the DNN model. Most of the state-of-theart countermeasures concentrate on the defensive distillation or parameter re-training, which require prior knowledge of the target DNN and/or the attacking methods and hence greatly limit their generality and usability. In this paper, we propose to defend against adversarial attacks by utilizing the input deformation and augmentation techniques that are currently widely utilized to enlarge the dataset during DNN's training phase. This is based on the observation that certain input deformation and augmentation methods will have little or no impact on DNN model's accuracy, but the adversarial attacks will fail when the maliciously induced perturbations are randomly deformed. We also use the ensemble of decisions to further improve DNN model's accuracy and the effectiveness of defending various attacks. Our proposed mitigation method is model independent (i.e. it does not require additional training, parameter finetuning, or any structure modifications of the target DNN model) and attack independent (i.e., it does not require any knowledge of the adversarial attacks). So it has excellent generality and usability. We conduct experiments on standard CIFAR-10 dataset and three representative adversarial attacks: Fast Gradient Sign Method, Carlini and Wagner, and Jacobian-based Saliency Map Attack. Results show that the average success rate of the attacks can be reduced from 96.5% to 28.7% while the DNN model accuracy is improved by about 2%.
Improved Gradient-Based Adversarial Attacks for Quantized Networks

Neural network quantization has become increasingly popular due to efficient memory consumption and faster computation resulting from bitwise operations on the quantized networks. Even though they exhibit excellent generalization capabilities, their robustness properties are not well-understood. In this work, we systematically study the robustness of quantized networks against gradient based adversarial attacks and demonstrate that these quantized models suffer from gradient vanishing issues and show a fake sense of robustness. By attributing gradient vanishing to poor forward-backward signal propagation in the trained network, we introduce a simple temperature scaling approach to mitigate this issue while preserving the decision boundary. Despite being a simple modification to existing gradient based adversarial attacks, experiments on multiple image classification datasets with multiple network architectures demonstrate that our temperature scaled attacks obtain near-perfect success rate on quantized networks while outperforming original attacks on adversarially trained models as well as floating-point networks.