Reviewerly/exHarmony huggingface dataset:
STRUCTURE OF DEVELOPMENT OF CONSTRUCTIVE SKILLS OF PRIMARY SCHOOL TEACHERS THROUGH COLLABORATIVE LEARNING

Structure of development of constructive skills of primary school teachers through collaborative learningThe article discusses the problems of professional training of a modern primary school teacher by innovative means.The aim of the study is to develop a model for the formation and development of constructive skills of future primary school teachers using joint teaching methods and analyze its impact on the teacher's professional skills.The method of joint learning involves joint solution of the problem, taking into account immersion in a common communicative context.
A Low-cost Mobile System with Multi-AR Guidance for Brain Surgery Assistance*

Surgical operation especially brain surgery requires comprehensive understanding on the surrounding area of the surgical path. Augmented Reality (AR) technology provided an effective way to increase the surgeon's perception on the plan. However, current applications were hindered by the expensive hardware and limited guidance information. In this paper, an AR system especially designed for brain surgery was proposed, which featured in low-cost system components and multi-AR guidance. A light-weight AR glasses was utilized together with normal mobile phone to provide mobile AR to the surgeon. A web-based application was implemented for compatibility of various mobile devices. Multi-AR information was designed for surgical guidance, including planned operation path, dangerous areas, and three quantitative guidance metrics. Patient's specific 3D model was reconstructed based on CT images, and the phantom was utilized to evaluate the effectiveness of the system. The experimental results indicated that the assistance of the multi-AR guidance outperformed the results of with no AR guidance at all and with virtual path guidance only. As a result, our system could help the operator to perform the operation tasks easier.Clinical Relevance- This proposed method provided a potential way for brain surgery with multiple AR guidance information with the assistance of a low-cost AR system, which may improve the surgeon's cognition on the surgical site.
Bayesian networks: generating independent samples

Given a Bayesian network with evidence, we propose an efficient majorizing function for the Acceptance-Rejection method to generate a sequence of independent samples from the posterior distribution. Because the proposed method generates independent samples, it has none of the problems associated with MCMC. The sequence allows for the easy calculations of the confidence intervals for the posterior quantities of interest. It can also yield independent inputs for the simulations of a bigger probabilistic system that includes the BN.
Gravity

Motion in curved spacetime, classical equations in covariant form, tidal forces, Einstein’s field equation in empty space, and weak (linearized) gravitation.
CITADINE: A Platform to Provide the Experiences of Survivors of Natural Disasters as Open Educational Resources for Risk Communication

This paper presents the concept, and the prototype of a platform that enables citizens from local communities to share their experiences with historic disasters online as open educational resources for teachers, community builders, urban planners, or volunteers. Key focus of the platform is to highlight the impact of disasters on citizens’ everyday lives. Based upon an overview of previous research, a requirements analysis, personas and related use cases, we present the insights into the prototypical implementation of the platform. We also provide a brief outlook upon possible practical uses of the platform, and its materials.
SensoMan: Social Management of Context Sensors and Actuators for IoT

Sensor networks that collect data from the environment can be utilized in the development of context-aware applications, bringing into sight the need for data collection, management, and distribution. Boards with microcontrollers, such as Arduino and Raspberry Pi, have gained wide acceptance and are used mainly for educational and research purposes. Utilizing the information available via sensors connected to these platforms requires extended technical knowledge. In this work, we present a sensor management framework, SensoMan, that manages a collection of sensors spread in the environment connected to microcontroller boards. We present the framework’s architecture, a method for sensor data management, and a prototype system. Sensor data can also trigger the execution of actions on actuators. Thus, we further propose a rule engine as well as social connectivity following a scheme where sensors and their data can be shared among users. Our work shows that the creation of such a system is feasible and can use simple equipment (e.g., sensors, controller plugs) that can be replicated in other environments. The use of SensoMan is demonstrated via two scenarios that show its potential in combining simple tools that do not require an extended learning curve. A small-scale user study was also performed.
Semi-supervised Subspace Metric Learning

Metric learning is a popular approach for measuring the similarity between samples and is essential for many machine learning tasks. However, its performance may be degraded when the data is corrupted by noise, the feature is redundant, or the data is heterogeneously distributed. To circumvent these limitations, we propose a semi-supervised subspace local metric learning algorithm. Specifically, we find a low-dimensional subspace by regarding the subspace learning as an inverse problem on Grassmannian manifold. Then we learn some local positive definite metrics on the subspace defined on different regions and optimize them using intrinsic steep decent method. We also introduce a manifold regularizer to preserve the data geometric structure. At last, our experiments show that, even on noisy datasets, we still improve the accuracy and robustness compared with the state-of-the-art metric learning algorithms.
RESEARCH INNOVATIVE QUALITIES OF HIGHER SCHOOL TEACHERS

This article examines the innovative qualities of a modern higher education teacher. Currently, training has acquired many new opportunities and continues to evolve. A modern teacher in our world should easily apply the latest approaches and techniques that contribute to the expansion and enrichment of the student's consciousness. The relevance of the research is due to the fact that a modern teacher must be ready for innovation and try something new. It is such a personal characteristic as innovation that contributes to the inclusion of a person in an innovative society, not only from the position of personal adaptation in it, but also inclusion as an active participant in processes. The article presents the results of a study of the degree of expression of the factors of innovative personality traits, such as creativity, orientation towards the future and risk for the sake of success of teachers of two areas of teaching.
Solar Noise in 40-year long Total Solar Irradiance Composite Time Series

Since the late 70's, successive satellite missions have been monitoring the sun's activity, recording total solar irradiance observations.These measurements provide estimates of the Earth's energy imbalance, i.e. the difference of energy absorbed and emitted by our planet.With this amount of TSI data, solar irradiance reconstruction models can be better validated which can also improve studies looking at past climate reconstructions (e.g., Maunder minimum).Various algorithms have been proposed to merge the various TSI measurements recorded over the last 4 decades.We develop a 3-step algorithm based on data fusion, including a stochastic noise model to take into account the short and long-term correlations.We develop a wavelet filter in order to eliminate specific correlations introduced by the data fusion.Comparing with previous products,the mean value difference is below 0.1 W/m2and the discrepancy with the solar minima is mostly below 0.05 W/m2.Next, we model the frequency spectrum of this 40-year TSI composite time series with a Generalized Gauss-Markov model(with white noise) due to an observe flattening at high frequencies.It allows us to fit a linear trend in these TSI time series by joint inversion with the stochastic noise model via a maximum-likelihood estimator.Our results show that the amplitude of such trend is -0.009+/-0.01W/(m2.yr).We conclude that the trend in these composite time series is mostly an artifact due to the solar noise.
An approach to image smoothing in the frame of signal model

The smoothing operation that preserves the image structure is one of prime stages in analysis and processing of incoming video information. According to accepted image model, smoothing is considered as splitting a signal into pair of components, which are named as textural and structural ones. To preserve the structure of an image, the smoothing operation must be performed independently in each of connected region. The criterion for connectivity of pair of points is the absence of contour edges between them. We propose edge-preserving image smoothing algorithm basing on detecting the borders that separate individual regions and on independent averaging within each of the regions.
Indirectly Named Entity Recognition

We define here indirectly named entities, as a term to denote multiword expressions referring to known named entities by means of periphrasis. While named entity recognition is a classical task in natural language processing, little attention has been paid to indirectly named entities and their treatment. In this paper, we try to address this gap, describing issues related to the detection and understanding of indirectly named entities in texts. We introduce a proof of concept for retrieving both lexicalised and non-lexicalised indirectly named entities in French texts. We also show example cases where this proof of concept is applied, and discuss future perspectives. We have initiated the creation of a first lexicon of 712 indirectly named entity entries that is available for future research.
A novel light strip extraction algorithm based on ridge tracking

As a non-contact three-dimensional measurement method, line structured light measurement is widely used in three-dimensional shape measurement due to its fast speed, high accuracy, and convenient implementation. The line structured light extraction is one of the most important step in the line structured light measurement system. Most traditional algorithms are based on the Hessian matrix or the light intensity distribution of the light strip section to extract the center. In this paper, a novel algorithm based on ridge tracking is proposed to extract the center of the light strip, the approximate direction has been used to estimate the direction of the light strip. Experiments have proved that this algorithm can be applied to the extraction of light strips in a variety of scenes, and has good accuracy and noise resistance.
The Application of Fuzzy Delphi Method (FDM) in the Development of Fun-Driven Mobile Learning (FDML) Model for Teaching of Arithmetic in Saudi Schools

One of the methods used in Design and Development Research (DDR) is the Fuzzy Delphi Method (FDM). It is a systematic method for the development and validation of the model. This article aims to use this method to validate a Fun-Driven Mobile Learning (FDML) Model for Teaching Arithmetic. In phase 1 of design and development research, based on the opinion of teachers as subject matter experts, the need to develop the FDML model was justified. In phase 2, the model developed, comprised of thirty-two teaching activities grouped into four clusters. This article presents results on suitability and overall usefulness of the model components derived from the collective opinion of the 15 experts involved in Phase 3 of the study. The data analysis was conducted through the fuzzy Delphi method using a 7-point Likert scale. The outcome indicates a consensus of 93.4 percent of experts at a threshold value of ≤0.02. The suggested components of the FDML model include an integral planning reference for the application of the model and provided alternative validation procedures for model developers in mathematics education and educational technology.
Techniques and Approaches to Generate Automatic Program Code

Raising the level of digital education is one of the main challenges facing the education system now and in the future. The use of modern technical means in the learning process is an important prerequisite for achieving this ambitious goal. However, this must go hand in hand with the introduction of new scientific methods and teaching approaches. This article presents different approaches which the author has applied in her practice with high school students. It proves the benefit of their implementation in the educational process.
Ketchup As You Like: Drawing Editor for Foods

Omelet rice is a popular Japanese dish made from an egg omelet filled with rice. There is a culture of freely drawing patterns or messages on it with tomato ketchup. The advantage of this practice is that it is easy to create originality and express creativity. We believe that utilizing this advantage would be possible to generate original food models in VR space easily. Therefore, we introduce a web-based real-time application called “Ketchup As You Like”, which allows users to freely draw with ketchup on an omelet image. This application is able to generate omelet images with realistic ketchup patterns. The image generation used in “Ketchup As You Like” is created by combining CNN-based segmentation and generative adversarial networks (GAN). Demo video is available at https://youtu.be/m6AjwEY6Jp8
Multi-robot Exploration System in Unknown Environment Based on Submap

When exploring the unknown environment, the efficiency of a single robot is too slow, while the map merging between robots is a difficulty when the initial pose of multiple robots is unknown. In order to solve these problems, we design a multi-robot exploration system in unknown environment based on submap, in which we design a robot map merging framework through the NDT-transformer network, The environment slam is implemented by Cartography, and the submap are transformed into 2D point clouds, which are matched and geometrically transformed by 2D- NDT-transformer to realize the map merging between robots, The average recall rate of the top 1% of the network is 7.75$\sim$ 11.77% higher than that of 2D-NetVLAD, and 12.44% $\sim$ 15.39% higher than that of 2D-PointNetVLAD. At the same time, in the aspect of path planning, we use a Multi-robot Potential Field RRT (MPRRT) exploration method, which combines RRT and artificial potential field, so that the exploration efficiency is greatly increased. Compared with ordinary RRT, the trajectory length is reduced by 4.9% $\sim$ 14%, the exploration time is reduced by 12% $\sim$ 17%, and the exploration efficiency is significantly improved.
Manipuri Meitei Mayek Numeral Classification by Using HOG-Assisted Deep Learned Features

In this paper, a histogram of oriented gradient (HOG)-assisted deep learning algorithm is proposed for classification of Manipuri Meitei Mayek (MMM) numerals. In HOG, gradient computes the magnitude and direction of largest change in intensity among small neighborhood of every pixel. The extracted HOG feature vectors are trained and recognized by using multilayer perceptron (MLP) artificial neural network. The stacking method is used to gain robust features. After that, it is applied to as Softmax classifier and aims for the recognition of MMM’s character. To prove the effectiveness of our work, various comparisons are made on existing state of the art of Manipuri optical character recognition (OCR) algorithms and it shows that our proposed algorithm is superior comparing to the other existing Manipuri OCR work.
Expressway Tunnel Operation Risk Assessment Model Based on Improved Critic Method and Cloud Model

The safety risk assessment of expressway tunnel operations, which is a multi-factor complex system evaluation problem, can be considered as a critical process of expressway operation management. From the perspective of the operating company, the risk evaluation index system of expressway tunnel operations can be established through actual operation conditions, and the objective weight of the evaluation index can be calculated through the CRITIC weighting method, after that, the digital characteristics of the risk index could be obtained through the cloud model. The comprehensive certainty value that belongs to each risk level could be gained, and the result should be determined according to the principle of maximum value. Finally this model could be applied to the overall risk assessment of the tunnel, and it is shown that the risk assessment result of expressway tunnel obtained by this model is similar to the actual operation conditions of expressway tunnels.
An Efficient Masked Face Classifier Using EfficientNet

The widening spread of an infectious disease, namely COVID-19 (corona virus disease 2019) has become a serious health problem for countries around the world. As an effort to control the confirmed case of COVID-19, WHO advice people to wear a face mask to protect themselves from the spread of the coronavirus. However, the people&#x0027;s awareness of wearing face masks in certain location is still low, so an automatic masked face classifier model is needed. This research performes the masked face classification into two class (masked and not-masked) by using the EfficientNet pretrained model. The experiment show that the EfficientNet is able to achieve, not only the best accuracy, but also the most efficient model, compared to the other state-of-the-art model of CNN, such as ResNet-50 and Inception. This condition is achieved when using both limited dataset and larger dataset. EfficientNet is able to achieve the 99&#x0025; accuracy with about 5x lower number of network parameters and 3x faster testing time than ResNet-50. Compare to Inception, EfficientNet is better in terms of accuracy and also efficiency.
Implication of Explanation Systems to Missionaries

One Biblical Case (Acts 28:1-6) Paul was at the island of Malta after escaping a shipwreck on his way to Rome. A viper was driven out by the heat fastened itself on Paul's hand. The islanders explained the incident as an evil omen, and it showed that Paul was a murderer. According to this explanation system, Paul was to die in a few minutes, but they changed their minds that he was a god when he didn't die. Their explanation system for such an incident is wrong, which was evidenced in the result it yielded. There is an accurate explanation of such an event. During rainy seasons reptiles hide under rubbles for warmth and safety. This biblical account indicates that our explanation system could be wrong.
Book review

Syntactic complexity is one of the key research foci in writing assessment. This study combines traditional syntactic complexity (SC) measures with newly-proposed dependency distance measures to better assess second language (L2) SC development. Based on a syntactically-annotated corpus of 400 compositions, we aim to investigate the extent to which the traditional SC measures and dependency distance measures can differentiate the writing proficiency of beginner, intermediate, and advanced learners. In terms of traditional SC measures, length-based measures fare better when pinpointing the different writing proficiency levels, but none of these can significantly differentiate all adjacent levels. As for dependency distance measures, the overall mean dependency distance can significantly discriminate all pairs of adjacent proficiency levels, serving as the best metric explored in our study. Moreover, dependency distance measures can further explain the findings of the traditional SC measures from the perspective of language processing.
Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.
Consolidated Knowledge Corpus: Design Technique

The article presents the results of a pilot study for the structural transformation of 18 Bachelor’s Degree Programs for e-learning using Consolidated Knowledge Corpus. Consolidated Knowledge Corpus is a multidimensional object-oriented structure of educational content objects and their connections. All academic topics from 224 disciplines were combined into a Consolidated Knowledge Corpus to unify similarities and eliminate duplication. A unified matrix of educational content for the first year of study for 18 bachelor’s programs was compiled and an optimized modular structure of the curriculum was built. As a result, the necessary production volume of digital educational products and subsequently the necessary volume of investments were reduced by 55 percent.
Monads for Measurable Queries in Probabilistic Databases

We consider a bag (multiset) monad on the category of standard Borel spaces, and show that it gives a free measurable commutative monoid.Firstly, we show that a recent measurability result for probabilistic database queries (Grohe and Lindner, ICDT 2020) follows quickly from the fact that queries can be expressed in monad-based terms.We also extend this measurability result to a fuller query language.Secondly, we discuss a distributive law between probability and bag monads, and we illustrate that this is useful for generating probabilistic databases.1. we show that the full language BALG is measurable, which allows us to also treat aggregation queries within the same framework, and
Mathematics of Reinforcement Learning

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Subjective Rate Distortion Optimization for Screen Content Coding Based on SVR

Screen content video is widely used in wireless display, remote desktop, video conference, etc. The massive data generated by these applications brings great pressure to transmission and storage, so it is an urgent need for efficient screen content coding (SCC) method for data compression. In this paper, a subjective rate distortion optimization method based on SVR is proposed for SCC, which significantly improves the compression efficiency of screen content. Firstly, we extract the structure texture feature set of the screen content image based on the visual characteristics. Then, we filter the appropriate texture features as the feature vector of SVR, and use SVR to train the optimal regression model. Finally, the regression model is used to predict the Lagrange multiplier of the coding block and improve the rate distortion optimization process. Experimental results show that compared to state-of-the-art method, the proposed method can save up to 9.46% bitrate cost under similar subjective quality.