Reviewerly/exHarmony huggingface dataset:
RETRACTED ARTICLE: Human motion capture system in sports performance based on Internet of Things technology and wireless inertial sensor

Abstract Taking human movements has very good prospects of application in sports, animated projects, medicine and health and other areas. This article aims to study the human motion capture system in sports performances based on the Internet of Things technology and wireless inertial sensors. This article first introduces the theory and characteristics of the Internet of Things and motion capture; next, according to the different characteristics of the sensors in the inertial motion capture system, a two-step Kalman filter is proposed to process the accelerometer and the magnetometer separately and, finally, the structure of this article. The human body motion model is used to analyze the acceleration dynamic error that occurs during the motion. In addition, an inertial motion capture system is constructed to obtain and visualize the structure of each motion node. The experimental results in this paper show that the Kalman filtering algorithm can ensure the accuracy of angle estimation under different motion states and has good fault tolerance to external interference. Among them, the error of the static state is reduced by 23.1%.
Constraint-based algorithm to estimate the line of a milling edge

Each practical task has its constraints. They limit the number of potential solutions. Incorporation of the constraints into the structure of an algorithm makes it possible to speed up computations by reducing the search space and excluding the wrong results. However, such an algorithm needs to be designed for one task only, has a limited usefulness to tasks which have the same set of constrains. Therefore, sometimes is limited to just a single application for which it has been designed, and is difficult to generalise. An algorithm to estimate the straight line representing a milling edge is presented. The algorithm was designed for the measurement purposes and meets the requirements related to precision.
Can Skeletal Joint Positional Ordering Influence Action Recognition on Spectrally Graded CNNs: A Perspective on Achieving Joint Order Independent Learning

3D skeletal based action recognition is being practiced with features extracted from joint positional sequence modeling on deep learning frameworks. However, the spatial ordering of skeletal joints during the entire action recognition lifecycle is found to be fixed across datasets and frameworks. Intuition inspired us to investigate through experimentation, the influence of multiple random skeletal joint ordered features on the performance of deep learning systems. Therefore, the argument: can joint order independent learning for skeletal action recognition practicable? If practicable, the goal is to discover how many different types of randomly ordered joint feature representations are sufficient for training deep networks. Implicitly, we further investigated on multiple features and deep networks that recorded highest performance on jumbled joints. This work proposes a novel idea of learning skeletal joint volumetric features on a spectrally graded CNN to achieve joint order independence. Intuitively, we propose 4 joint features called as quad joint volumetric features (QJVF), which are found to offer better spatio temporal relationships between time series joint data when compared to existing features. Consequently, we propose a Spectrally graded Convolutional Neural Network (SgCNN) to characterize spatially divergent features extracted from jumbled skeletal joints. Finally, evaluation of the proposed hypothesis has been experimented on our 3D skeletal action KLHA3D102, KLYOGA3D datasets along with benchmarks, HDM05, CMU and NTU RGB D. The results demonstrated that the joint order independent feature learning is achievable on CNNs trained on quantified spatio temporal feature maps extracted from randomly shuffled skeletal joints from action sequences.
An effective contract as an innovative contour for evaluating the results of a teacher's professional activity in higher school

The purpose of the research is to reveal the world experience of using the effective contract system as an innovative contour for evaluating the results of the professional activity of a teacher in higher education. Results and discussion. The concept of an effective contract has been disclosed and its functions have been defined. The historical roots of the introduction of the effective contract policy abroad and in Russia have been revealed. The points of view on the evaluation of the results of professional activity of a teacher in a higher school abroad have been shown. The main attention has been paid to the comparative analysis of the foreign and domestic practice of effective contracts. The theoretical approaches underlying the development of an effective contract model have been determined, the advantages and prospects of an effective contract in modern realities have been identified.
Separating Data Examples by Description Logic Concepts with Restricted Signatures

We study the separation of positive and negative data examples in terms of description logic concepts in the presence of an ontology. In contrast to previous work, we add a signature that specifies a subset of the symbols that can be used for separation, and we admit individual names in that signature. We consider weak and strong versions of the resulting problem that differ in how the negative examples are treated and we distinguish between separation with and without helper symbols. Within this framework, we compare the separating power of different languages and investigate the complexity of deciding separability. While weak separability is shown to be closely related to conservative extensions, strongly separating concepts coincide with Craig interpolants, for suitably defined encodings of the data and ontology. This enables us to transfer known results from those fields to separability. Conversely, we obtain original results on separability that can be transferred backward. For example, rather surprisingly, conservative extensions and weak separability in ALCO are both 3ExpTime-complete.
Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

With the increasing importance of mathematics in basic education, how to evaluate and analyze the intelligent effect of mathematics teaching classroom through scientific methods has become one of the indicators to evaluate the intelligent classroom. This paper studies the design and application of mathematics teaching intelligent classroom based on the PCA-NN (principal component analysis-neural network) algorithm. Firstly, this paper briefly describes the current research status of mathematics teaching intelligent classroom design and PCA-NN algorithm. Secondly, combined with the key factors of mathematics teaching, it formulates specific standards and puts forward an adaptive strategy of intelligent and personalized intelligent mathematics teaching classroom. Finally, the algorithm is verified by experiments. The results show that, for students with different mathematics basic levels, the mathematics teaching intelligent classroom based on the PCA-NN algorithm can effectively improve the quality of mathematics classroom teaching. Through the research on the factors such as teaching quality, effect, and interaction mode involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting teaching quality are determined. This paper analyzes and studies the system from different angles. The research results provide some help for the current quality evaluation of classroom teaching and use the PCA-NN algorithm to make quantitative analysis and multivariate verification of mathematics classroom teaching effect.
Landslide Risk Classification Based on Ensemble Machine Learning

Landslides are common natural disasters that often cause serious impact and damage to human society. Since landslide disasters threaten people's production and life all the time, it is particularly important to predict the risk of landslides and to control landslide disasters. When studying landslide risk and deciding whether to treat the landslide, it is meaningful to classify and compare the risk of landslides so as to select those landslides with a higher degree of danger for priority treatment. The target of this paper is to extract factors related to landslide risk, and train a classification models for landslide risk. It employs ensemble machine learning algorithms to classify landslide hazards. Because the landslide feature has a large number of dimensions, this paper uses the PCA method to reduce the dimension. Due to the imbalance of the samples, this paper uses the SMOTE method to handle the imbalanced learning. The results of study show that the selected factors are highly related to landslide risk, the classification model in this paper has good accuracy.
Adaptive dual graphs and non-convex constraint based embedded feature selection

In traditional feature selection methods, to guarantee the sparsity of the rows, ${l}_1$-norm or ${l}_{2,1}$-norm is often used to constrain the evaluation matrix. As convex regularization terms, they work well in most cases. However, when dealing with redundant features, some non-convex regularization terms tend to show better performance. With the advantages of adaptive manifold learning and non-convex constraints, a novel algorithm is proposed in this paper, called adaptive dual graphs and non-convex constraint based embedded feature selection (DNEFS). With the framework of sparse regression, DNEFS preserves the manifold structure information of both data space and feature space, simultaneously. Meanwhile, by using the principle of information entropy, the local manifold information in dual graphs can be learned and updated adaptively, resulting in a better feature selection effect. Different from traditional convex constraints, a novel non-convex regularization term is introduced in this paper. This regularization term consists of the difference between ${l}_{2,1}$-norm and Frobenius norm, and is written as ${l}_{2,1-2}$-norm. By using this novel regularization term, DNEFS can handle redundant features more efficiently. Then, an alternating iterative updating method is used in this paper to optimize the objective function, and six benchmark datasets are used to test the performance of DNEFS. By comparing with six comparison algorithms, the experimental results show that DNEFS can achieve better performance.
The Effects of Program Satisfaction Attributes on Attention Restoration and Behavioral Intention of Rural Healing Tourists: Mediating Role of Personnel Services and Moderating Role of Age

As health-seeking tourism is on the increase worldwide, various healing programs using natural-based healing resources have been developed. The study aims to examine the structural relationships between the program satisfaction attributes, attention restoration, and behavioral intention of rural healing tourists. In particular, the study focuses on mediating effect of personnel services and moderating effect of age.<BR> A total of 433 rural healing program participants from the seven rural healing tourism villages answered the surveys. The results indicated that program satisfaction attributes(i.e., relaxation, interaction, natural-seeking) had a positive effect on attention restoration through personnel services rather than directly, which in turn affect behavioral intention. Tourists’ age also moderated the relationships between program satisfaction and personnel services. The results of the study imply that personnel services play an important role in effectively increasing healing tourists’ attention restoration and intention. Findings suggest that different strategy based on age should be developed in order to enhance the effects of rural healing programs.
Analysis of Detections by the Earthquake Network App between 2017-12-15 and 2020-01-31

The 'Earthquake Network’ (EQN) is an app which detects earthquakes by creating an ad-hoc network of smartphones' accelerometer sensors and provides early warnings for earthquakes via the same smartphone app. Detections are not due to individual smartphone measurements but due to near-simultaneous trigger signals from clusters of smartphones running the app. Therefore detections are normally located in the closest populated regions to an earthquake's epicentre. These datasets compare sets of detections with the earthquake parameters published by seismic institutes in order to analyse the performance of the EQN network.One dataset contains 550 detections made by EQN between 2017-12-15 and 2020-01-31 in Chile, USA and Italy. Wherever possible, each detection was associated with an earthquake from the parameter catalogue of each country's seismic institute (CSN for Chile, USGS for USA and INGV for Italy). Associations were carried out automatically but also checked manually.The other dataset contains 134 detections from around the world that could be associated to earthquakes with magnitude ≥ M5 or magnitude ≥ M4.5 in Italy and the USA. There are 68 detections that are common to the first dataset. All detections were associated to parameters from the the USGS earthquake parameter catalogue for consistency.
Systematic Approach for Efficient Use of Research Methods in Educational Theory

The purpose of this study was to change the broad picture of research methods in the educational theory, which is essential for development of modern pedagogy. The systematic approach to education developed by the scientific school of L.I. Novikova, member of the Russian Academy of Education, was analyzed. The authors explored the differentiated indicators such as integrity, orderliness and evolvability, that shape the research of all social pedagogical systems. This article emphasizes that in the modern-day world it is important to integrate different research methods (theoretical and empirical, quantitative and qualitative) and combinations of research methods in pedagogical and related sciences in order to make the best use of their complementarity, with regard to the special nature of the subject. Emphasis should be given to qualitative research methods guided by the intention to understand the personal meanings lying behind a child’s actions. The two most useful qualitative methods are the method of participatory observation and the method of real-life situations. The article discusses various methods of research of soft systems in the educational theory aimed at the identification, proper study and multifaceted analysis of the problems that occur. It also emphasizes the importance of a systematic approach in the theory of collective education where the backbone of the system is the children’s community and the unity of children and adults.
An Improved Slice Reconciliation Protocol for Continuous-Variable Quantum Key Distribution

Reconciliation is an essential procedure for continuous-variable quantum key distribution (CV-QKD). As the most commonly used reconciliation protocol in short-distance CV-QKD, the slice error correction (SEC) allows a system to distill more than 1 bit from each pulse. However, the quantization efficiency is greatly affected by the noisy channel with a low signal-to-noise ratio (SNR), which usually limits the secure distance to about 30 km. In this paper, an improved SEC protocol, named Rotated-SEC (RSEC), is proposed through performing a random orthogonal rotation on the raw data before quantization, and deducing a new estimator for the quantized sequences. Moreover, the RSEC protocol is implemented with polar codes. The experimental results show that the proposed protocol can reach up to a quantization efficiency of about 99%, and maintain at around 96% even at the relatively low SNRs (0.5,1), which theoretically extends the secure distance to about 45 km. When implemented with the polar codes with a block length of 16 Mb, the RSEC achieved a reconciliation efficiency of above 95%, which outperforms all previous SEC schemes. In terms of finite-size effects, we achieved a secret key rate of 7.83×10-3 bits/pulse at a distance of 33.93 km (the corresponding SNR value is 1). These results indicate that the proposed protocol significantly improves the performance of SEC and is a competitive reconciliation scheme for the CV-QKD system.
Research on the Optimization of College English Classroom Teaching under the Network Environment

Under the background of the Internet era, network technology is widely applied to all levels of society, which makes information-based teaching become a new mode of modern education. With the help of the network environment, the university classroom is more and more diversified, which subverts the traditional classroom teaching mode, and makes the quality and efficiency of classroom teaching significantly improved. Based on college English classroom, this paper discusses how to optimize college English teaching by using network resources, and truly realizes the development of information-based teaching.
Empirical Findings on the Role of Structured Data, Unstructured Data, and their Combination for Automatic Clinical Phenotyping.

The objective of this study is to explore the role of structured and unstructured data for clinical phenotyping by determining which types of clinical phenotypes are best identified using unstructured data (e.g., clinical notes), structured data (e.g., laboratory values, vital signs), or their combination across 172 clinical phenotypes. Specifically, we used laboratory and chart measurements as well as clinical notes from the MIMIC-III critical care database and trained an LSTM using features extracted from each type of data to determine which categories of phenotypes were best identified by structured data, unstructured data, or both. We observed that textual features on their own outperformed structured features for 145 (84%) of phenotypes, and that Doc2Vec was the most effective representation of unstructured data for all phenotypes. When evaluating the impact of adding textual features to systems previously relying only on structured features, we found a statistically significant (p < 0.05) increase in phenotyping performance for 51 phenotypes (primarily involving the circulatory system, injury, and poisoning), one phenotype for which textual features degraded performance (diabetes without complications), and no statistically significant change in performance with the remaining 120 phenotypes. We provide analysis on which phenotypes are best identified by each type of data and guidance on which data sources to consider for future research on phenotype identification.
High-Efficient Syndrome-Based LDPC Reconciliation for Quantum Key Distribution

Quantum key distribution (QKD) is a promising technique to share unconditionally secure keys between remote parties. As an essential part of a practical QKD system, reconciliation is responsible for correcting the errors due to the quantum channel noise by exchanging information through a public classical channel. In the present work, we propose a novel syndrome-based low-density parity-check (LDPC) reconciliation protocol to reduce the information leakage of reconciliation by fully utilizing the syndrome information that was previously wasted. Both theoretical analysis and simulation results show that our protocol can evidently reduce the information leakage as well as the number of communication rounds.
Two-stage energy auction model and strategy among multiple microgrids for trading incentives and default management

The lack of trust caused by rigid pricing strategies, third-party participation, and transaction default may lead to a decline in transaction willingness and misgivings about the availability of resources among microgrids (MGs). In particular, if large-scale transaction default occurs, it may endanger the safe and stable operation of the power grid. In this paper, a two-stage energy auction model based on blockchain and smart contracts to solve the problem of lack of trust and transaction default, and to increase flexibility in energy transactions among microgrids is proposed. The novelty of the proposed method lies in the transaction incentives and management of payment defaults through smart contracts and the partition management of buyer MGs' energy demands. Partitioning energy demands in the first stage of the auction can increase the diversity of sellers' bidding in the second stage. A credit management model based on the completion of historical transactions is designed to manage defaults. The priority factor affecting transaction order and energy allocation is designed to incentivize transactions. The introduction of blockchain can reduce dependence on trusted third parties and solve the security and privacy issues of transactions. Smart contracts are used to host the entire process of the auction and record, calculate the priority of each microgrid before the auction starts, and allocate energy fairly. In the energy delivery stage, the smart contract determines whether there is a default and automatically realizes the transfer of transaction funds, avoiding any selfish behavior in the transaction. Finally, we verify the effectiveness of the above-mentioned trading method through numerical analysis.
ImbTreeEntropy: An R package for building entropy-based classification trees on imbalanced datasets

In this paper, we propose a novel R package, named ImbTreeEntropy, for building binary and multiclass decision trees using generalized entropy functions, such as Rènyi, Tsallis, Sharma–Mittal, Sharma–Taneja and Kapur, to measure the impurity of a node. These are important extensions of the existing algorithms that usually employ Shannon entropy and the concept of information gain. Additionally, ImbTreeEntropy is able to handle imbalanced data, which is a challenging issue in many practical applications. The package supports cost-sensitive learning by defining a misclassification cost matrix and weighted sensitive learning. It accepts all types of attributes, including continuous, ordered and nominal attributes. The package and its code are made freely available.
ASSESSMENT OF THE USE OF CLOUD-ORIENTED OPEN SCIENCE SYSTEMS IN THE DOMESTIC EDUCATIONAL SPACE

The Budapest Open Access Initiative and the Berlin Declaration are analyzed. The main provisions of the policy of the international movement for open access are given. One of the priorities of science development in Ukraine is integration into the European Research Area. 6 priorities of integration of Ukrainian science into the European Research Area are described. A survey of mathematics teachers of advanced training courses of Kryvyi Rih State Pedagogical University and students of the National University of Life and Environmental Sciences in 2020-2021 academic year were conducted. The current state of the use of cloud-oriented systems of open science is clarified. The main factors to be considered for further implementation of these systems in teacher education and their ICT competence increase are identified.
Deep Adversarial Reinforcement Learning With Noise Compensation by Autoencoder

We present a new adversarial learning method for deep reinforcement learning (DRL). Based on this method, robust internal representation in a deep Q-network (DQN) was introduced by applying adversarial noise to disturb the DQN policy; however, it was compensated for by the autoencoder network. In particular, we proposed the use of a new type of adversarial noise: it encourages the policy to choose the worst action leading to the worst outcome at each state. When the proposed method, called deep Q-W-network regularized with an autoencoder (DQWAE), was applied to seven different games in an Atari 2600, the results were convincing. DQWAE exhibited greater robustness against the random/adversarial noise added to the input and accelerated the learning process more than the baseline DQN. When applied to a realistic automatic driving simulation, the proposed DRL method was found to be effective at rendering the acquired policy robust against random/adversarial noise.