Reviewerly/exHarmony huggingface dataset:
Formation of the Electronic Informational and Educational Environment in the Prospects of the University 4.0

The aim of the study is justified by the need to create modern personnel competencies for the digital economy and create a new model of modern universities.Strengthening the role of the university, as one of the main producers of knowledge and competencies for the information society, dictates the need for it to implement both the functions of education and research, as well as the functions of transfer, commercialization of technology, and a provider of knowledge about the future.The purpose of the study is to analyse the role of electronic forms of learning and the functioning of the electronic educational system in the prospects for the establishment of University 4.0, and also to identify what the electronic information and educational environment in a new generation of higher education should be like.The methodological basis of the work is the analytical and systemic approaches to the study of the object of study using the methods of logical and causal analysis, expert assessments and systematization of data.The article discusses the specific experience of the development and implementation of e-learning in Peter the Great St. Petersburg Polytechnic University (SPbPU).The authors analyse the effectiveness of the interaction of participants in the educational process within the framework of the electronic information educational environment, changes in the personnel policy, issues of staff development, the creation of a developed ICT infrastructure and the prospects for promoting large-scale modernization of the university.Based on the analysis of the experience of development and implementation at electronic information educational environment in SPbPU, the article highlights the advantages and disadvantages of using digital technologies in the educational activities of the university considering the challenges of its modernization.The authors concluded that the electronic educational environment is effective both in the educational process of the university and in increasing the competitiveness of the university in the modern digital market, in fulfilling the functions of transfer, commercialization of technologies and a provider of knowledge about the future.
Form and Function of Hand Gestures for Interpretation and Generation

This paper investigates the relation between the form and function of hand gestures in audio and video recordings of American and English political iscourse of different type. Gestures have an important function in face-to-face communication contributing to the successful delivery of the message by reinforcing what is expressed by speech or by adding new information to what is uttered. The relation between form and function of gestures has been described by some of the pioneers of gestural studies. However, since gestures are multifunctional and they must be interpreted in context, it is important to investigate to what extent the form of gestures can be used to interpret their function automatically. Individuating the relation between form and function of gestures is also important for generating appropriate gestures in various communicative situations and this knowledge is vital for the integration of machine-human communicative and cognitive functions. In this paper we show that the automatic classification of the semiotic types of hand gestures using their shape description is quite successful and this is an important step towards their interpretation in face-to-face communication as well as their interpretation and generation in advanced multimodal interactive systems. More specifically in the present work we annotated the semiotic types of hand gestures produced by five politicians in different contexts, adding this information to existing multimodal annotations. Then, we trained machine learning algorithms to identify the semiotic type of the hand gestures. The F1 score obtained by the best performing algorithms on the classification of four semiotic types is 0.7 outperforming the F1 score of 0.59 obtained in a preceding pilot study which addressed the identification of three semiotic types of hand gestures produced by a speaker in a small dataset.
Black-box Attacks on Automatic Speaker Verification using Feedback-controlled Voice Conversion

Automatic speaker verification (ASV) systems in practice are greatly vulnerable to spoofing attacks. The latest voice conversion technologies are able to produce perceptually natural sounding speech that mimics any target speakers. However, the perceptual closeness to a speaker's identity may not be enough to deceive an ASV system. In this work, we propose a framework that uses the output scores of an ASV system as the feedback to a voice conversion system. The attacker framework is a black-box adversary that steals one's voice identity, because it does not require any knowledge about the ASV system but the system outputs. Experimental results conducted on ASVspoof 2019 database confirm that the proposed feedback-controlled voice conversion framework produces adversarial samples that are more deceptive than the straightforward voice conversion, thereby boosting the impostor ASV scores. Further, the perceptual evaluation studies reveal that converted speech does not adversely affect the voice quality from the baseline system.
K- Nearest Neighbors based Automatic Modulation Classifier for Next Generation Adaptive Radio Systems

This paper presents a verity of K-Nearest Neighbors (KNN) classifiers along with different statistical features in order to classify MPSK and MQAM signals.Further, the performance of proposed KNN classifiers with different values of 'K' and distance functions are analyzed under non-ideal channel conditions.Finally, to prove the superiority of the proposed KNN classifiers the performance is compared with that of the literature approaches at various SNR values.
M-Learning as a New Interactive Technology in Education

The field of information technology, in particular, the development of mobile applications, is by far the most dynamically developing in the world.The development of mobile technologies is associated with constant challenges aimed at meeting the needs of customers, providing the most modern and relevant services.This is especially true for education.The relevance of the topic is predetermined by the strategic course of action of Russia -special attention was paid to the significance of the topic for Russian reality, since the Russian economy is aimed at developing both the knowledge economy and improving its competitive position in countries with developed economies.The article presents the author's vision of how mobile learning is changing the vector of the educational environment, complementing the existing generally accepted educational process with new digital software technologies, involving both teachers and students themselves to keep up with the times.The aim of the work was to study the most modern mobile technologies presented on the educational services market, which have a beneficial effect on the development of soft skills and related competencies among the younger generation in accordance with new and modern requirements of personal and professional development and compatibility with innovative economic initiatives.The provision of results and coverage of the topic will provide an essential basis for further actions to develop soft skills programs and integrate them into the educational process.The materials of this article are of theoretical and practical importance and will be useful to teachers when developing their courses.
Partition games

We introduce cut, the class of 2-player partition games. These are nim type games, played on a finite number of heaps of beans. The rules are given by a set of positive integers, which specifies the number of allowed splits a player can perform on a single heap. In normal play, the player with the last move wins, and the famous Sprague–Grundy theory provides a solution. We prove that several rulesets have a periodic or an arithmetic periodic Sprague–Grundy sequence (i.e. they can be partitioned into a finite number of arithmetic progressions of the same common difference). This is achieved directly for some infinite classes of games, and moreover we develop a computational testing condition, demonstrated to solve a variety of additional games. Similar results have previously appeared for various classes of games of take-and-break, for example octal and hexadecimal; see e.g. Winning Ways by Berlekamp, Conway and Guy (1982). In this context, our contribution consists of a systematic study of the subclass ‘break-without-take’.
Control of the transition frequency of a superconducting flux qubit by longitudinal coupling to the photon number degree of freedom in a resonator

We control transition frequency of a superconducting flux qubit coupled to a frequency-tunable resonator comprising a direct current superconducting quantum interference device (dc-SQUID) by microwave driving. The dc-SQUID mediates the coupling between microwave photons in the resonator and a flux qubit. The polarity of the frequency shift depends on the sign of the flux bias for the qubit and can be both positive and negative. The absolute value of the frequency shift becomes larger by increasing the photon number in the resonator. These behaviors are reproduced by a model considering the magnetic interaction between the flux qubit and dc-SQUID. The tuning range of the transition frequency of the flux qubit reaches $\approx$ 1.9 GHz, which is much larger than the ac Stark/Lamb shift observed in the dispersive regime using typical circuit quantum electrodynamics devices.
HNAS: Hierarchical Neural Architecture Search on Mobile Devices.

Neural Architecture Search (NAS) has attracted growing interest. To reduce the search cost, recent work has explored weight sharing across models and made major progress in One-Shot NAS. However, it has been observed that a model with higher one-shot model accuracy does not necessarily perform better when stand-alone trained. To address this issue, in this paper, we propose a new method, named Hierarchical Neural Architecture Search (HNAS). Unlike previous approaches where the same operation search space is shared by all the layers in the supernet, we formulate a hierarchical search strategy based on operation pruning and build a layer-wise operation search space. In this way, HNAS can automatically select the operations for each layer. During the search, we also take the hardware platform constraints into consideration for efficient neural network model deployment. Extensive experiments on ImageNet show that under mobile latency constraint, our models consistently outperform state-of-the-art models both designed manually and generated automatically by NAS methods.
Information-theoretic analysis for transfer learning

Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different distributions (denoted as $\mu$ and $\mu'$, respectively). In this work, we give an information-theoretic analysis on the generalization error and the excess risk of transfer learning algorithms, following a line of work initiated by Russo and Zhou. Our results suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence $D(mu||mu')$ plays an important role in characterizing the generalization error in the settings of domain adaptation. Specifically, we provide generalization error upper bounds for general transfer learning algorithms and extend the results to a specific empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase. We further apply the method to iterative, noisy gradient descent algorithms, and obtain upper bounds which can be easily calculated, only using parameters from the learning algorithms. A few illustrative examples are provided to demonstrate the usefulness of the results. In particular, our bound is tighter in specific classification problems than the bound derived using Rademacher complexity.
Application of face expression recognition technology in skilled unsupervised course based on ultra-wide regression network

In traditional teaching, people can quickly guess the emotion type of the other person based on facial expressions but understanding the human facial expression by computer is a very complicated problem. There may be some differences in the expression of each person. Therefore, how to make the comp uter 'read and understand' the emotional state of the person according to the facial expression of the person is the research focus of this paper. In this paper, the expression recognition based on dynamic sequence is developed, and the mapping relationship between basic expression and emotion is studied to construct the emotional model, and the emotional state recognition of the learner is realized by the research on facial expression. In the intelligent teaching environment, the teacher adjusts the teaching strategy according to the emotional state recognition test results, improves the teaching efficiency, and realizes the wisdom teaching. Moreover, combined with the actual situation, an expression recognition algorithm based on the ultra-wide regression network model for unsupervised learning classroom education is constructed. Through experimental analysis, we can know that this research algorithm has certain advantages in facial expression recognition.
Fundamental Limits of Deep Graph Convolutional Networks

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate the capabilities and limitations of GCNs, we investigate their power, as a function of their number of layers, to distinguish between different random graph models (corresponding to different class-conditional distributions in a classification problem) on the basis of the embeddings of their sample graphs. In particular, the graph models that we consider arise from graphons, which are the most general possible parameterizations of infinite exchangeable graph models and which are the central objects of study in the theory of dense graph limits. We give a precise characterization of the set of pairs of graphons that are indistinguishable by a GCN with nonlinear activation functions coming from a certain broad class if its depth is at least logarithmic in the size of the sample graph. This characterization is in terms of a degree profile closeness property. Outside this class, a very simple GCN architecture suffices for distinguishability. We then exhibit a concrete, infinite class of graphons arising from stochastic block models that are well-separated in terms of cut distance and are indistinguishable by a GCN. These results theoretically match empirical observations of several prior works. To prove our results, we exploit a connection to random walks on graphs. Finally, we give empirical results on synthetic and real graph classification datasets, indicating that indistinguishable graph distributions arise in practice.
Weaknesses and Challenges of Network Neutrality Measurement Tools

Network neutrality has been the subject of a hot debate worldwide. Regulation has been implemented in many countries to enforce the principle of a neutral network. But compliance to the rules has to be checked thanks to specific measurement tools. This paper aims at highlighting the weaknesses of current network neutrality measurement tools and at providing hints on challenges to be addressed on the topic.
The roles of teachers in the digital age school

Contemporary changes in school, especially student-centered teaching, require teachers to play new roles in digitalization the teaching process.To respond to new challenges in teaching, a teacher needs to have a number of different digital competences.For this purpose, research was conducted with the aim of determining the attitudes of teachers on the importance of digital competences for successful realization of teaching.The survey was conducted on a sample of 566 primary school teachers in Serbia.The Likert Attitude Scale was applied in the study.Data were processed using descriptive statistical procedures.The results of the research show that new roles to be acquired are based on the acquisition of skills in: integration of eLearning in the school teaching and learning system, ability to use electronic resources in the teaching process, ability to apply electronic devices and media in teaching, ability to apply European informatics standards.The results obtained may represent a sound basis, for policy makers, in modeling a quality school and effective teaching that would lead each student to success according to his or her individual capabilities.
Uncertainty visualization using maps for nuclear and radiological emergencies

Visual uncertainty representation and communication during nuclear and radiological emergencies face empirical and theoretical challenges. The work carried out focuses on visualisation of uncertainties as a decision support and communication tool. We represent uncertainty visually through maps and visual robustness indicators. An interdisciplinary approach was applied where both quantitative and qualitative methods were used to validate and test the tools developed in 6 different occasions/countries. The principles for building effective uncertainty representation and communication tools are presented. It is important to note that the visual approach offers a great opportunity to represent uncertainty to experts in the field of emergency planning especially in the nuclear/radiological one.
MVStylizer

Recent research has made great progress in realizing neural style transfer of images, which denotes transforming an image to a desired style. Many users start to use their mobile phones to record their daily life, and then edit and share the captured images and videos with other users. However, directly applying existing style transfer approaches on videos, i.e., transferring the style of a video frame by frame, requires an extremely large amount of computation resources. It is still technically unaffordable to perform style transfer of videos on mobile phones. To address this challenge, we propose MVStylizer, an efficient edge-assisted photorealistic video style transfer system for mobile phones. Instead of performing stylization frame by frame, only key frames in the original video are processed by a pre-trained deep neural network (DNN) on edge servers, while the rest of stylized intermediate frames are generated by our designed optical-flow-based frame interpolation algorithm on mobile phones. A meta-smoothing module is also proposed to simultaneously upscale a stylized frame to arbitrary resolution and remove style transfer related distortions in these upscaled frames. In addition, for the sake of continuously enhancing the performance of the DNN model on the edge server, we adopt a federated learning scheme to keep retraining each DNN model on the edge server with collected data from mobile clients and syncing with a global DNN model on the cloud server. Such a scheme effectively leverages the diversity of collected data from various mobile clients and efficiently improves the system performance. Our experiments demonstrate that MVStylizer can generate stylized videos with an even better visual quality compared to the state-of-the-art method while achieving 75.5× speedup for 1920×1080 videos.
A New Side-Channel Attack on Reduction of RSA-CRT Montgomery Method Based

RSA-CRT is one of the most common algorithms in the digital signature. Several side-channel attacks have been presented on the implementation of RSA-CRT. One of the most important side-channel attacks on RSA-CRT is Modular Reduction on Equidistant Data (MRED). The implementation of RSA-CRT has too many challenges in the multiplications when the key size is too long (e.g. 2048 bits). Montgomery multiplication is one of the common methods for executing the RSA multiplication, which has many implementation problems and side-channel leakage challenges. This article first implements an RSA-CRT algorithm based on the Montgomery multiplication with the high-speed and low area hardware. The implementation is named RSA-CRT-MMB (Montgomery Method Based). Next, a new power analysis side-channel attack on RSA-CRT-MMB is presented. We name our attack MRED on MMB. The attack utilizes new side-channel leakage information about the CRT reduction algorithm implemented by the MMB, for the first time. The previous articles do not investigate the MRED attack on Montgomery multiplication in RSA-CRT. Finally, a new countermeasure is presented to prevent the MREDM attack. The countermeasure does not have any overload in the hardware area or running time of the RSA algorithm. The correctness of our scheme, the 2048-bit RSA-CRT-MMB, is investigated by the implementation of the scheme on the SASEBO-W board in our DPA laboratory. The total running time of 2048-bit RSA is 250[Formula: see text]ms and the RSA algorithm occupies only 23% of LUT slice on Spartan-6 FPGA. The proposed countermeasures are also verified by practical experiments.
A Study on the Development of the Competency-based Curriculum for College Liberal Arts Education －Focusing on the case of D** College

This study presents a model through the case of D-college for how to develop competency-based Liberal Arts courses in colleges.Since 2014, many colleges have developed and operated an NCS-based curriculum.However, although NCS-based education reflected the characteristics of colleges, it also resulted in the weakening of Liberal Arts education for those colleges.For institutions of higher learning, the importance of Liberal Arts education is relatively low, due to short lectures and a major-oriented education system.At a time when the curriculum of colleges is being transformed into a competency-based one, it is very urgent to change the Liberal Arts education of colleges.The process of developing Liberal Arts education courses at D-college is based on the ADDIE model, but the development process at each stage focused on capacity centers.This article looked at the specific progress of the 'Analysis-Design -Development' phase.In the analysis phase, the external environment, internal capacity, and satisfaction of the educational needs were analyzed, while the core competency of the colleges in question was checked.In the design phase, Liberal Arts education goals and policies were established, and Liberal Arts education areas were set.In the development phase, competency-based Liberal Arts subjects were derived and a curriculum profile was developed.As a result, D-college has been able to derive a variety of competency-based subjects, with several tasks remaining for the education to operate effectively.If organizations, human resources, Liberal Arts and math are secured, the competency-based Liberal Arts education process can be operated systematically.