Reviewerly/exHarmony huggingface dataset:
Educational activities: structural and content analysis of the concept in the national scientific traditions

During 2020, at the level of legislative initiatives, there is an active discussion about the content of education and outreach activities. Due to the «vagueness» of the concepts of «education» and «educational activity» in academic circles, it is difficult to differentiate various kinds of activities that are valuable in broadcasting high-quality and meaningful information to a wide audience. The authors of the article consider the essential features of education in relation to the objectives of non-formal education and the principles set forth in the Model Law of 2002. The purpose of the article is to propose the author's definition of the concepts of «education» and «educational activity», considering the educational and educational potential in the conditions of mediatization of information and communication, leisure and educational environment. The empirical basis of the article is the materials of scientific publications, the content of the Model Law «On educational activities» and the Concept of educational activities presented in «Education for the future». Research results: 1. The authors considered the connection between education and educational activities, carried out their substantive analysis, determined their mission and revealed their mechanisms and principles of organization. 2. The qualitative component of educational activities is considered both at the level of providing the opportunity to carry out worldview, value-orientated, educational functions, and at the level of particular practices. 3. The principles of educational activities are determined.
Towards practical intrusion detection system over encrypted traffic*

Privacy and data confidentiality are today at the heart of many discussions. But such data protection should not be done at the detriment of other security aspects. In the context of network traffic, intrusion detection system becomes totally blind when the traffic is encrypted, making clients again vulnerable to known attacks. To reconcile security and privacy, BlindBox and BlindIDS are proposed to perform Deep Packet Inspection over an encrypted traffic, based on two different cryptographic techniques. But, on one side, even if BlindBox is quite efficient to detect an anomalous encrypted traffic, it necessitates a very high setup time for clients and servers and does not protect the know-how of Security Editors (SEs) working on detection rules. On the other side, BlindIDS does protect SE's market and does not introduce any latency during setup time, but is definitely not enough efficient for a practical use. Herein, it is shown that the design of a fully efficient and market-compliant intrusion detection system over an encrypted traffic is possible. The system is based on only symmetric cryptography, and permits to encrypt a packet of 1500 bytes in about 6 μs and to test such packets with 3000 rules in less than 2 μs.
On Quantum States with a Finite-Dimensional Approximation Property

We consider a class (convex set) of quantum states containing all finite rank states and infinite rank states with the sufficient rate of decreasing of eigenvalues (in particular, all Gaussian states). Quantum states from this class are characterized by the property (called the FA-property) that allows to obtain several results concerning finite-dimensional approximation of basic entropic and information characteristics of quantum systems and channels. We obtain a simple sufficient condition of the FA-property. We show that this property implies finiteness of the von Neumann entropy, but leave unsolved the question concerning the converse implication. We obtain uniform approximation results for characteristics depending on a pair (channel, input state) and for characteristics depending on a pair (channel, input ensemble). We establish the uniform continuity of the above characteristics as functions of a channel w.r.t. the strong convergence provided that the FA-property holds either for the input state or for the average state of input ensemble.
A Novel Stock Index Prediction Approach by Combining Extreme Learning Machine With Symbiosis Organisms Search Algorithm

This paper presents a robust, effective metaheuristic technique called Symbiotic organisms search (SOS) algorithm which is incorporated with extreme learning machines (ELM) model to enhance the forecasting performance in the stock market. The stock market is a time series data which is highly uncertain and volatile in nature. Due to ELM's fast learning process and high accuracy, it is mostly used in regression and classification problems in regards to other traditional methods in a neural network. ELM is a supervised learning algorithm that depends upon the number of hidden neurons and also the weights and biases within the input and hidden layer. Selection of appropriate number of hidden neurons decides the prediction ability of ELM model. Therefore, in this article initially, the ELM model is run several times with different numbers of neurons in hidden layer to suitably fix the number of hidden neurons for three different stock indices. Then metaheuristic techniques such as SOS, teaching learning based optimization (TLBO), differential evolution (DE), and particle swarm optimization (PSO) are implemented to optimally design the weights and biases of EML models. The performance of SOS-ELM is compared with that of TLBO-ELM, DE-ELM, and PSO-ELM in predicting the next day closing price of stock indices. Several statistical measures such as MSE (mean square error), MAPE (mean absolute percentage error), and accuracy are used as performance measures. A parametric test called paired sample t-test is used to show the effectiveness of SOS-ELM model over other methods.
Correction to: Social Engineering Attacks During the COVID-19 Pandemic

A correction to this paper has been published: https://doi.org/10.1007/s42979-021-00550-7
Musical Education aspects in the contemporary school based on the Theory of Multiple Intelligence

What is intelligence? Which are the most important characteristics of it? Starting from these two questions that have a powerful impact over the researchers, Gardner fulfill to present a new meaning sense of the termen - intelligence, which continues to be discussed. During the article we will present some connections between the musical education systems of the 20th-21st centuries and the Theory of Multiple Intelligences. Even though some of them appeared before the theory, through his affirmations, Gardner manages to prove that the musical intelligence isn’t just a talent, it is in every human being. The study of the Theory Multiple Intelligence and the deepening of some of the well-known music education systems led to design and develop of some attractive and efficient music activities in school.
A Research to Integrate Baoding Red Culture into College English Teaching

As the Chinese people's precious cultural heritage, the red culture deserves the Chinese younger generation's worship and inheritance.However, current college English teaching doesn't play a positive role in the inheritance of Chinese red culture.This paper takes red culture education in Baoding district as an example, discusses the significance of introducing red culture into college English teaching, presents the current problems of the integration of red culture into college English teaching, and finally puts forward countermeasures to the above-mentioned problems.Hopefully, this paper can provide some insight into how to effectively integrate red culture into college English teaching.
Tagging Tone for Mandarin Pinyin Based on Sequence Labelling

Generally, fundamental frequency (F0) is applied as the clue for tone recognition in normal speech. Tone recognition in whisper speech without F0 could be based on the temporal and spectral cues. However, the Mandarin Electro-Laryngeal (EL) speech with fixed F0 has no tone information. Therefore, the tone recognition of Mandarin EL speech is so difficult. And the researches about tone recognition for Mandarin EL speech is insufficient. In this paper, a new method labelling the tone for pinyin is proposed based on the context information to identify the tone of Mandarin speech without tone information. The experiment result shows that the precision, recall and the F value are all above 97% based on the test dataset. The amount of semantic information influences the performance of proposed method. If the amount of semantic information is little, the accuracy of tone labelling would be poor. The result shows that the proposed method has a good precision and robustness. The method can label the tone for pinyin without any tone information only based on the context information. The proposed method can label tones for tonal language, such as the Mandarin speech.
Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks

We utilize a connection between compositional kernels and branching processes via Mehler’s formula to study deep neural networks. This new probabilistic insight provides us a novel perspective on the mathematical role of activation functions in compositional neural networks. We study the unscaled and rescaled limits of the compositional kernels and explore the different phases of the limiting behavior, as the compositional depth increases. We investigate the memorization capacity of the compositional kernels and neural networks by characterizing the interplay among compositional depth, sample size, dimensionality, and non-linearity of the activation. Explicit formulas on the eigenvalues of the compositional kernel are provided, which quantify the complexity of the corresponding reproducing kernel Hilbert space. On the methodological front, we propose a new random features algorithm, which compresses the compositional layers by devising a new activation function.
Improved Multi Target Tracking in MIMO Radar System Using New Hybrid Monte Carlo–PDAF Algorithm

This article deals with the multi-target tracking problem (MTT) in MIMO radar systems. As a result, this problem is now seen as a new technological challenge. Thus, in different tracking scenarios, measurements from sensors are usually subject to a complex data association issue. The MTT data association problem of assigning measurements-to-target or target-state-estimates becomes more complex in MIMO radar system, once the crossing target tracking scenario arises, hence the interference phenomenon may interrupt the received signal and miss the state estimation process. To avoid most of these problems, we have improved a new hybrid algorithm based on particle filter called “Monte Carlo” associated to Joint Probabilistic data Association filter (JPDAF), the whole approach named MC-JPDAF algorithm has been proposed to replace the traditional method as is known by the Extended KALMAN filter (EKF) combined with JPDAF method, such as EKF-JPDAF algorithm. The obtained experimental results showed a challenging remediation. Where, the MC-JPDAF converges towards the accurate state estimation. Thus, more efficient than EKF-JPDAF. The simulation results prove that the designed system meets the objectives set for MC-JPDA by referring to an experimental database using the MATLAB Software Development Framework.
DEVELOPMENTAL POTENTIAL OF ARTISTIC SYNTHESIS IN THE ART EDUCATION OF A FUTURE TEACHER

The synthesis of the arts is not the latest concept, but at the beginning of the XXI century it acquired a unique, compared to other eras, role in the artistic culture of modern society. Modern artistic practices are characterized by new synthetic properties, and therefore approaches to the mastery of modern teachers of modern art forms should take into account the relevant features of perception of works of art and synesthetic features of the process of artistic creation. Updating the technologies of art education and its methodological basis in the context of artistic synthesis is a factor in effectively solving the problems of artistic training of future teachers.
Applicability of information technology in libraries with a step ahead to smart library in 21st century

Smart is a network enabling free access to all libraries those are digital in electronic society. Smart libraries is a behavior change initiative for cyber safety and wellbeing designed to equip libraries and connect library users with the skills they need for smart, safe and responsible use of technology. Being smart means knowing how to guard against security and privacy risks online, download content in a legal and ethical way, research and reference information, as well as manage reputation and relationships in cyberspace. Smart helps to embed a culture of positive technology use, create policies, procedures, and gain access to evidence-informed resources and track progress in becoming smart. The term internet becomes a greater part of normal life and libraries are the knowledge centers at the forefront of providing necessary services to access and assist the community. Smart helps libraries providing a good environments where people can experience feelings of safety, become part of actual and virtual communities and acquire the much needed skills. We think community-based action such as smart libraries will truly drive e-safety as a cultural norm in communities through worldwide.Keywords: Community, Cyber-safety, Smart Library, Skill, Technology
A Defeasible Deontic Logic for Pragmatic Oddity

We introduce a variant of Deontic Defeasible Logic to handle the issue of Pragmatic Oddity. The key idea is that a conjunctive obligation is allowed only when each individual obligation is independent from the violation of the other obligations. The solution makes essential use of the constructive proof theory of the logic while maintaining a feasible computational complexity.
Facial Occlusion Detection and Reconstruction Using GAN

We developed an architecture considering the problem of the presence of various objects like glasses, scarfs, masks, etc. on the face. These types of occluders greatly impact recognition accuracy. Occluders are simply treated as a source of noise or unwanted objects in an image. Occlusion affects the appearance of the image significantly. In this paper, a novel architecture for the occlusion removal of specific types like glasses is dealt with. A synthetic mask for this type of occlusion is created and facial landmarks are generated which are further used for image completion method is used to complete the image after removing the occluder. We have trained the model on celebA dataset, which is available publicly. The experimental results show that the proposed architecture for the occlusion removal effectively worked for the faces covered with glasses or scarfs.
Unsupervised Learning Algorithms for Hydropower’s Sensor Data

Anomaly detection is a subfield of machine learning where a model is produced which pays special attention to any variations from the norm in the data. Anomaly detection has a wide variety of uses in different domains such as fraud detection, intrusion detection, event detection in sensor networks, etc. This paper will study anomaly detection in time series and some techniques such as one-class SVM and isolation forest which are used to identify the anomalous data points in the time-series. We present our comparative study on unsupervised machine learning algorithms which are effective for monitoring machines in hydropower plants using the data which are generated from the sensors.
Classification Of Batik Images Using Multilayer Perceptron With Histogram Of Oriented Gradient Feature Extraction

Abstract. Batik is one of the hereditary cultural heritages which has a high aesthetic value and a deep philosophy. Batik is one of the cultural icons from Indonesia which was awarded as a cultural heritage from UNESCO on October 2, 2009. Currently, Indonesian batik has various types of motifs and different patterns, which are scattered in Indonesia with their names and meanings. The many batik patterns in Indonesia make it difficult to identify batik motifs, especially for the common people. To overcome this problem, it is necessary to have a batik classification system with a high level of accuracy, so that everyone can recognize the batik pattern easily. In this study, the Histogram Of Oriented Gradient method was used as a feature extraction process to obtain batik density and Multilayer Perceptron as a classification method to determine the level of accuracy. The result of the level of accuracy obtained for each batik motif has a different level of accuracy. This is because the batik motifs have unique patterns and shapes that are not specific. The level of income obtained was 83.4 %. From the results of the study concluded that the use of the Histogram Of Oriented Gradient method as a feature extraction method, the Multilayer Perceptron as a classifier can be applied for image classification on batik.
Outpatient Text Classification System Using LSTM

Outpatient text classification is an important problem in medical natural language processing. Existing research has conventionally focused on rule-based or knowledge-source-based feature engineering, but only a few studies have utilized the effective feature learning capabilities of deep learning methods. A long short-term memory (LSTM) model for the outpatient text classification system was proposed in this research. The system has the ability to classify outpatient categories according to textual content on website Taiwan E Hospital. The experimental results showed that our system has very well in the task. The success of the LSTM model applications in the outpatient system provide users to inquire about their health status as references.
Possibilities of the Mixed Research Methods Application in the Management of a Modern University (On the Materials of Sociological Research)

The paper discusses the possibilities of using mixed methods research in the management of a modern university. The goal of the study is to substantiate the heuristic potential of mixed methods research to identify effective approaches to managing the faculty in modern conditions. The study describes iterative data analysis within the framework of a mixed strategy:1) standard quantitative analysis; 2) quantitative triangulation, including the use of several methods, such as the Importance Performance Analysis (IPA); 3) standard qualitative analysis; 4) mixed triangulation, which ultimately led to a synergistic effect. The research results lie in different ways, including methodological and content related. The methodological results include the identification of the special heuristic potential of mixed methods during exploratory research; in the substantive part, a situation of social maladjustment of faculty was diagnosed in the context of developing “academic capitalism”, when the academic community, taking into account both economic and increasing social deprivation, is demotivated for effective labor activity.
A Detailed Analysis of Semantic Dependency Parsing with Deep Neural Networks

The use of Long Short Term Memory (LSTM) networks continues to yield better results in natural language processing tasks. One area which recently has seen significant improvements is semantic depen ...
Hybrid spotted hyena–Nelder-Mead optimization algorithm for selection of optimal machining parameters in grinding operations

Abstract In this research, a novel optimization algorithm, which is a hybrid spotted hyena-Nelder-Mead optimization algorithm (HSHO-NM) algorithm, has been introduced in solving grinding optimization problems. A well-known grinding optimization problem is solved to prove the superiority of the HSHO-NM over other algorithms. The results of the HSHO-NM are compared with others. The results show that HSHO-NM is an efficient optimization approach for obtaining the optimal manufacturing variables in grinding operations.
SRecGAN: Pairwise Adversarial Training for Sequential Recommendation

Sequential recommendation is essentially a learning-to-rank task under special conditions. Bayesian Personalized Ranking (BPR) has been proved its effectiveness for such a task by maximizing the margin between observed and unobserved interactions. However, there exist unobserved positive items that are very likely to be selected in the future. Treating those items as negative leads astray and poses a limitation to further exploiting its potential. To alleviate such problem, we present a novel approach, Sequential Recommendation GAN (SRecGAN), which learns to capture latent users’ interests and to predict the next item in a pairwise adversarial manner. It can be interpreted as playing a minimax game, where the generator would learn a similarity function and try to diminish the distance between the observed samples and its unobserved counterpart, whereas the discriminator would try to maximize their margin. This intense adversarial competition provides increasing learning difficulties and constantly pushes the boundaries of its performance. Extensive experiments on three real-world datasets demonstrate the superiority of our methods over some strong baselines and prove the effectiveness of adversarial training in sequential recommendation.
Financial time series forecasting using AdaBoost-GRU ensemble model

일반적으로 금융 시계열 (financial time series) 예측은 비선형성 (non-linearity)과 불규칙성(irregularity)으로 인해 매우 어려운 일이다. 본 논문에서는 금융 시계열 예측을 위해 AdaBoost 알고리즘과 GRU 모형을 결합한 하이브리드 앙상블 학습 방법 (hybrid ensemble learning approach)을 제안하고자 한다. 여기서 GRU 모형은 LSTM (long short term memory) 모형과 함께 시계열 예측에 널리 사용되는 RNN (recurrent neural network)의 변형 모형이다. 우리는 KOSPI 데이터와 원/달러 환율과 같은 금융 시계열 데이터를 가지고 제안된 모델을 평가하고자 한다. 성능실험 결과 제안된 AdaBoost-GRU 앙상블은 3가지 척도 즉, MAE, MSE 및 RMSE 척도에서 기존의 ARIMA 모형, LSTM 모형, GRU 모형, 그리고 Adaboost-LSTM 앙상블보다 좋은 성능을 보였다. 그리고 Adaboost-LSTM 모형과의 처리속도 면에서 제안된 AdaBoost-GRU 모형이 빠름을 알 수 있었다.
Modular operators and entanglement in supersymmetric quantum mechanics

The modular operator approach of Tomita-Takesaki to von Neumann algebras is elucidated in the algebraic structure of certain supersymmetric quantum mechanical systems. A von Neumann algebra is constructed from the operators of the system. An explicit operator characterizing the dual infinite degeneracy structure of a supersymmetric two dimensional system is given by the modular conjugation operator. Furthermore, the entanglement of formation for these supersymmetric systems using concurrence is shown to be related to the expectation value of the modular conjugation operator in an entangled bi-partite supermultiplet state thus providing a direct physical meaning to this anti-unitary, anti-linear operator as a quantitative measure of entanglement. Finally, the theory is applied to the case of two-dimensional Dirac fermions, as is found in graphene, and a supersymmetric Jaynes Cummings Model.