Reviewerly/exHarmony huggingface dataset:
Digitization of Education: a Socio-Pedagogical Experiment from the Perspective of Social Philosophy

The introduction of components of distance education in terms of the current pandemic is seen as a rather unexpected but successful socio-pedagogical experiment on the way to global digital education. This article is aimed to emphasize the possibilities of using the distance learning format as a space and a means of continuous monitoring of the educational process. This format allows establishing countless feedbacks between the participants in the process, implementing a synergistic approach at the local and global levels. Some results obtained in the course of surveys conducted by the faculty of the Department of Philosophy, Sociology and Religious Studies in the KSPU named after V.P. Astafiev are cited. These results testify, in particular, to some positive changes in attitudes towards the realities of “the remote” by both teachers and students. Moreover, such polls help to correct the process in practice and can serve as tracers of a wide range of not only technological problems, but essentially socio-philosophical ones related to the emergence of a new system of values. The notion that the digitalization of education is a promising strategy generally contributing to the development of civilization towards the dominance of post-material values is promoted.
Fine-Grained Few-Shot Classification with Feature Map Reconstruction Networks.

In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and significant accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet benchmark with minimal bells and whistles.
Leadership Uniformity in Raft Consensus Algorithm

The Raft consensus algorithm constitutes a widely-used algorithm not only in the broader area of distributed systems, ut also in private/permissioned blockchains such as Hyperledger Fabric. A Raft-based distributed system (RDS) strongly relies on leader election, which involves a number of time-related parameters. In the Raft-related literature, the process according to which those parameters are set is an under-researched area. Specifically, the use of the uniform distribution is the dominant approach. Motivated by this realization, in this work, we focus on these time parameters proposing the notion of “leadership uniformity” in combination with a series of performance metrics. Leadership uniformity is based on the desirable characteristic of having equality among the nodes who serve as leaders. The proposed performance metrics are straightforward adaptations of widely-used measurements from broad disciplines such as estimation theory. The experimental results of this work justify the appropriateness of the proposed notion of leadership uniformity. Specifically, the best performance was yielded by the utilization of normal distribution from which the time parameters under investigation were drawn.
Behavioural Equivalence of Game Descriptions

Game description language is a logical language designed for General Game Playing. The language is highly expressive so that, in theory, all finite-state games with perfect information and deterministic actions can be described. However, a game can be described in different ways and the way of description can dramatically affect behaviour of general game players. This paper investigates the relationships of game models and game descriptions. We first introduce the concept of submodel bisimulation to filter out unreachable states while maintain the nature of a game. We then define equivalence of game descriptions in the sense that two game descriptions are equivalent if the described games behaviourally the same. The concept of game equivalency, which breaks through logical equivalency, sets a boundary for reformulation of game descriptions. Finally we use a well-known strategy game, Hex Game, to demonstrate how to verify equivalence of game descriptions.
iSignDB: A database for smartphone signature biometrics

The signature has long been in use for the user verification. These signatures have user specific features that differentiate the individual for authentication. The signature verification can be offline or online. The offline verification considers only the static features of the signatures through the signature image, while the online verification considers various dynamic features associated with the signature such as pen pressure, pen tilt angle, velocity, acceleration, pen up and pen down, etc at various time stamps which are recorded using special digitizing tablets such as Wacom devices (STU-500, STU-530 and DTU-1031) [1,14] etc. In todays scenario, smartphones are widely used world-wide, and come equipped with various sensors e.g. accelerometer, gyroscope, magnetometer, GPS, etc. able to capture sensor logs and have been used widely in the literature to capture the dynamics of users’ behaviour while a signer signs on his smartphone. However, there is scarcity of publicly available databases for the online signatures collected using smartphone. In the present work, we describe biometric signature dataset iSignDB captured using smartphone. The iSignDB [6,10] consists of the genuine signature samples of a user as well as the skilled forgery samples where imposter was given multiple attempts to mimic the mannerisms of the original signer before giving skilled forgery samples. A total of 30 samples towards the genuine signature over 3 sessions with 10 samples per session while 15 samples of the skilled forgery with 5 samples per session were collected. Each of the session were at least 15 days apart. The iOS and Android based smartphones (namely iPhone7 and Redmi Note 7) were used for the data collection. The sensors used to collect this data, present in the smartphone are the gyroscope, magnetometer, GPS, and accelerometer. Smartphones having sensors any one lesser than these four, were not used for data collection, in order to have consistent number of features under each sample. They generate the following sensor readings: angular velocity, acceleration, orientation, geomagnetic field in the x, y, and z directions, position, which is collected using the MATLAB Mobile App installed in the smartphone, that sends the data to a licensed MathWorks cloud account in the form of a multitude of sensor logs. Each sample has image of the signature along with sensor readings. Some of the publicly available smartphone biometric signature databases are DooDB [2], MOBISIG [3], eBioSign DS 2 [7], etc. in which at least acceleration sensor reading is present but the iSignDB ensures these five of the sensor readings (acceleration, angular velocity, magnetic field, orientation, position) under each sample. This dataset can be successfully used to design smartphone biometric signature authentication system which is robust against a number of spoof attacks [11], [12], [13], [14]. As every user has a unique way of handling his/her smartphone which varies over different level of emotional intelligence of the user over a time period, this dataset can also be used for behavioural analysis of the users.
Spontaneous symmetry breaking and Husimi Q-functions in extended Dicke model

We study the emergence of a parity breaking coherent photonic state of a photon mode coupled to finite array of two-level systems, represented by pseudospins 1/2. The pseudospin-photon interaction is realised via a shift of the photonic oscillator equilibrium position by an amount linear in Cartesian component of the total pseudospin. We demonstrate analytically, that the instability is manifested in an upturn from concave to convex of the ground state energy dependence on the total pseudospin component coupled to the photons. The perturbation, sufficient for parity breaking, tends to zero in the ultrastrong limit of light-matter coupling. We present phase diagram of finite pseudospin-photon system, that demonstrates this feature. Evolution of Husimi Q-functions of the pseudospin and photon subsystems, and of the pseudospin entropy, along different trajectories across the phase diagram is presented.
Classification of Road Surface Quality Based on SVM Method

Abstract Damaged and potholes roads can occur due to rain puddles, too many heavy vehicles, poor asphalt quality, or maybe long road life. Damaged roads can hamper activities and endanger the safety of road users. It is necessary to monitor road quality periodically which is conducted by government, so that roads improvement can be done quick as possible. The aim of this study is to build a system that can classify roads surface quality. Support Vector Machine (SVM) classifications method is used to classify roads based on roadworthiness. In this study, 300 road surface data which contains good/smooth and damage quality of road are used. The simulation results show that SVM model can classify road surface data into two classes with average accuracy of 93%. The results can be a recommendation for government to prioritize which roads need to be improved.
Training Students under the Conditions of the Coronavirus Infection Pandemic: Advantages and Disadvantages

The article deals with the problem of introducing distance education in Russia in the context of the coronavirus pandemic. The author reveals the advantages and disadvantages of distance learning in comparison with the usual, traditional, in the higher education system. The article concludes that in a pandemic, distance learning has become a salvation for the higher education system, although in general it has a number of disadvantages. In a pandemic, the transition from the traditional system to distance learning was a salvation for education in general. However, its emergency introduction showed that for the usual traditional Russian education system using only insignificant digital learning technologies, this became a great stress both for the students and for the teachers themselves.
Exploring the role of progressive web applications in modern web development

Abstract This paper explores such a Web development technology as Progressive Web Applications. It is explained what this technology is and the purpose of it. The paper also compares progressive web applications to other Web technologies that are closely related to them, such as responsive websites and native applications. It highlights the most important differences and similarities between them. Furthermore, the paper discusses the advantages as well as the possible limitations of this technology. Finally, it provides some thoughts on whether or not this technology is the best solution for the online presence of a business.
Content based image retrieval with multi-channel LBP and colour features

Content based image retrieval (CBIR) systems are used for retrieving relevant images from datasets in response to a query based on image features. The features used for describing the image content play crucial role in determining the efficacy of the CBIR. In this paper a texture-colour fusion method exploiting the multi-channel information of colour images is proposed for describing the image content. Texture is represented with multi-channel local binary adder pattern, computed by considering all the channels of a colour image, and colour information is computed by quantising the constituent colour channels of the image. The method exploits RGB colour space for feature extraction. Experimental results show respective average retrieval precisions of 80.73%, 60.096%, 48.22% and 69.89% in the Wang's, Corel 5k, Corel 10k and Zubud datasets using the proposed feature combination. Comparative analysis indicates that the proposed approach has an edge over many other recent methodologies under consideration.
Optimization of Compute Costs in Hybrid Clouds with Full Rescheduling

Hybrid clouds enable cost optimizations when handling fluctuating workloads. Assuming that the private cloud is cheaper than the pay-per-use public cloud when it is constantly used, a cost-optimal hybrid operation means that the private cloud handles the base workload and the public cloud the workload peaks. The tasks that cause the workload have to be scheduled such that the private cloud's usage is maximized. Ideally, the scheduler knows the future tasks and can plan for maximizing the private cloud's usage. However, obtaining such an accurate prediction of the future is not always possible.We propose an approach called full rescheduling that allows online schedulers to move already running tasks from the public to the private cloud in order to save costs. We further present an assessment method for studying the economic benefit of full rescheduling and apply it in an industry case study for a data processing application. The study's results show an economic benefit due to an increased usage of the private cloud instead of the public cloud. The method also provides a cost-optimal number of nodes for the private cloud.
Data-Free Model Extraction

Current model extraction attacks assume that the adversary has access to a surrogate dataset with characteristics similar to the proprietary data used to train the victim model. This requirement precludes the use of existing model extraction techniques on valuable models, such as those trained on rare or hard to acquire datasets. In contrast, we propose data-free model extraction methods that do not require a surrogate dataset. Our approach adapts techniques from the area of data-free knowledge transfer for model extraction. As part of our study, we identify that the choice of loss is critical to ensuring that the extracted model is an accurate replica of the victim model. Furthermore, we address difficulties arising from the adversary's limited access to the victim model in a black-box setting. For example, we recover the model's logits from its probability predictions to approximate gradients. We find that the proposed data-free model extraction approach achieves high-accuracy with reasonable query complexity -- 0.99x and 0.92x the victim model accuracy on SVHN and CIFAR-10 datasets given 2M and 20M queries respectively.
Video-Based Crowd Counting Using a Multi-scale Optical Flow Pyramid Network

This paper presents a novel approach to the task of video-based crowd counting, which can be formalized as the regression problem of learning a mapping from an input image to an output crowd density map. Convolutional neural networks (CNNs) have demonstrated striking accuracy gains in a range of computer vision tasks, including crowd counting. However, the dominant focus within the crowd counting literature has been on the single-frame case or applying CNNs to videos in a frame-by-frame fashion without leveraging motion information. This paper proposes a novel architecture that exploits the spatiotemporal information captured in a video stream by combining an optical flow pyramid with an appearance-based CNN. Extensive empirical evaluation on five public datasets comparing against numerous state-of-the-art approaches demonstrates the efficacy of the proposed architecture, with our methods reporting best results on all datasets.
Directed acyclic graph fuzzy nonparallel support vector machine

Classification problem is an important research direction in machine learning. Nonparallel support vector machine (NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. When solving multi-class classification problems, NPSVM will encounter the problem of sample noises, low discrimination speed and unrecognized regions, which will affect its performance. In this paper, based on the multi-class NPSVM model, two improvements are made, and a directed acyclic graph fuzzy nonparallel support vector machine (DAG-F-NPSVM) model is established. On the one hand, for the noises that may exist in the data set, the density information is used to add fuzzy membership to the samples, so that the contribution of each samples to the classification is treated differently. On the other hand, in order to reduce the decision time and solve the problem of unrecognized regions, the theory of directed acyclic graph (DAG) is introduced. Finally, the advantages of the new model in classification accuracy and decision speed is verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of this new method.
Digital teacher competencies and their indicators

The implementation of the national program “Digital Economy of the Russian Federation”, the use of distance technologies in the educational process require teachers to have digital competencies. At the moment in the Russian Federation, there are no formulations of digital competencies of a teacher. Although different models of digital competencies and skills have been created, they are rather complementary to each other. An organization can integrate a ready-made model, but it may not fully meet all the characteristics of the organization, its architecture and internal structure. In Russia, in the field of education, the formulation of digital competencies is spontaneous. In Russian science, digital competence refers to the use of digital technologies in professional activities, training, and social communications. Digital competencies are skills for effectively solving problems of a professional, social, personal nature using various types of information and communication technologies. Using the Delphi peer review method, the authors developed the teachers digital competencies and their indicators. The value of the results obtained lies in the fact that when conducting competitive procedures for filling vacant teacher positions, it is possible to assess the level of formation of digital competencies.
Transfer Function Optimization for a Nonlinear-optical Loop Mirror (NOLM)-based Optical Reservoir

We investigate a nonlinear-optical loop mirror-based reservoir computing with real, imaginary and absolute-value mappings. The lowest mean-square error of 2.849×10 −10 is achieved for time prediction task when the optimized coupling-ratio of 0.76.
RDF2Vec Light – a lightweight approach for knowledge graph embeddings

Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge graphs, i.e. all nodes and edges. This leads to very high computational requirements on large graphs such as DBpedia or Wikidata. However, for most downstream application scenarios, only a small subset of concepts is of actual interest. In this paper, we present RDF2Vec Light, a lightweight embedding approach based on RDF2Vec which generates vectors for only a subset of entities. To that end, RDF2Vec Light only traverses and processes a subgraph of the knowledge graph. Our method allows the application of embeddings of very large knowledge graphs in scenarios where such embeddings were not possible before due to a significantly lower runtime and significantly reduced hardware requirements.
Data Glove Design and Virtual Hand Simulation Based on Single-chip Microcomputer

With the gradual development of virtual reality (VR) technology, users have increasingly higher demand for system interaction. The human-computer interaction (HMI) by means of data glove is a widely used interaction means in the current days. In this paper, a type of single-chip microcomputer based low cost data glove is designed for figure posture acquisition. Also, a programmable control virtual hand model is designed for simulation. And they are used for building a basic HMI system. In terms of hardware, the system adopts a single-chip microcomputer as the core of data processing and collects, processes and transmits hand posture data together with peripheral data acquisition circuit. In terms of software, mapping is established between the practical and virtual hand postures based on the strap-down navigation algorithm and the 3D skeletal animation algorithm, for the purpose of basic interactive application of data gloves.
Mobile Applications as Digital Support Material for the Inclusion of Students with Special Educational Needs

The right to access a regular education by students with special educational needs has been met in a limited way, because the Ecuadorian educational institutions have not the necessary support elements that strengthen the learning of these students. The present study focuses on the use of mobile applications that allow an educational inclusion of this vulnerable group of society. The mixed quantitative-qualitative method was applied, analyzing the data with the descriptive statistics and the hierarchical cluster method and the surveys applied to the teachers of the eighth grade of basic education of the Educational Unit “Presidente Velasco Ibarra” of Ibarra City, on the use of the two applications developed to be evaluated (WOOW ABC and READSIGNS). The results show that mobile applications allow greater participation and educational inclusion on the part of students with special educational needs, both in the academic activities of the classroom, as in the subjects taught by teachers of the regular education system.
Implementasi Media Pembelajaran Berbasis ICT dalam Proses Pembelajaran Matematika di Sekolah Menengah

For the presentation of the teachers' material more effectively and efficiently; therefore, it needs media learning that can motivate and stimulate students to morale in learning. One of them is by making use of ICT-based learning or media that we often call it TIK. This research aims to describe how the implementation of ICT-based learning media in learning mathematics in high school. This research is a research library. This research's nature is descriptive-i.e. decomposition analysis regularly, then the whole concept of giving comprehension and explanation to taste the result description. The analysis of the data used in this study analyzes the content (content analysis). The results of the research study this library is; (1) the existence of the benefits of ICT-based learning media in education; (2) the presence of the benefits of ICT-based learning media in the process of learning mathematics in high school.
Exploring a Learning Architecture for General Game Playing

General Game Playing (GGP) is a platform for developing general Artificial Intelligence algorithms to play a large variety of games that are unknown to players in advance. This paper describes and analyses GGPZero, a learning architecture for GGP, inspired by the success of AlphaGo and AlphaZero. GGPZero takes as input a previously unknown game description and constructs a deep neural network to be trained using self-play together with Monte-Carlo Tree Search. The general architecture of GGPZero is similar to that of Goldwaser and Thielscher (2020) [4] with the main differences in the choice of the GGP reasoner and the neural network construction; furthermore, we explore additional experimental evaluation strategies. Our main contributions are: confirming the feasibility of deep reinforcement for GGP, analysing the impact of the type and depth of the underlying neural network, and investigating simulation vs. time limitations on training.