Reviewerly/exHarmony huggingface dataset:
STEGANOGRAPHIC EMBEDDING OF DATA IN HTML DOCUMENT CODE

A two-key scheme for concealed embedding of data in open source HTML pages is proposed. The first key is used to encrypt the embedded message. The second key is required to encode the hidden message. The body structure of a web page is a tag tree. The embedding algorithm uses class tags. Empty classes do not affect the display of a web page in a browser. Hidden data is encoded by adding new vertices to the tag tree. New vertices are added to the leaf vertices of the page that serves as the container. There are leaf vertices that exclude the addition of empty classes to them. Each tag encodes eight bits of hidden information. The embed key is used to form class names that include new tags. Class names are disguised as the framework used on the web page. Names are generated using a hash function and an embedding key. The message extraction algorithm is based on knowledge of embedding and encryption keys by the receiving subscriber. The algorithm for extracting data boils down to searching for tags by their names and then decoding them. The developed algorithm can be used for transmitting hidden messages on the Internet. The proposed algorithm is open. Resistance is ensured by the secrecy of encryption and embedding keys. Direct enumeration is necessary to hack the system if the tag embedding key is unknown. The algorithm for forming class names does not violate the statistical patterns of the legal class names set. Resistance is provided by the key retention policy. The average throughput of the algorithm is 0,003 bits per byte.
The effect of university students' volunteer participation motivation on social capital: Mediating effect of volunteer activity satisfaction

The purpose of this study is to analyze the effect of volunteer activity participation motivation on social capital, focusing on the mediating effect of volunteer activity satisfaction amonguniversity students who participated in volunteer activities. The subjects of this study were 444university students who participated in volunteer activities. For data analysis, frequency analysis,correlation analysis, and regression analysis were performed using the SPSS 21.0 program. As a result of the analysis, first, there was a significant positive correlation between universitystudents' motivation to participate in volunteer activity, satisfaction with volunteer activity, andsocial capital. Second, it was found that university students' volunteer activity satisfaction had a partial mediating effect on the relationship between volunteer activity participation motivationand social capital. This study is meaningful in that it provides basic data in setting a way toimprove social capital through volunteer activities in university social volunteer education and toset a direction for revitalization of social volunteer.
THE IMPLEMENTATION OF MIND MAPPING TECHNIQUE IN IMPROVING STUDENTS’ MOTIVATION OF WRITING

This research is motivated by the lack of student motivation in writing report texts. This study aims to increase the writing motivation of class IX D students of SMP Negeri 1 Kabuh by using the Mind Mapping technique. This technique was chosen to increase student motivation so that students are enthusiastic in expressing their ideas in writing. To achieve this goal, the researcher determined the criteria for success, more than 70% of students were motivated during the writing process. This classroom action research uses the Kemmis and Taggart model using 4 steps, namely planning, implementation, observation, and reflection. This research was conducted in two cycles. Data were obtained through observation sheets and questionnaires. The results of the study indicate that the Mind Mapping technique is able to increase students' motivation in writing report texts. This can be proven by increasing student motivation. In the pre-writing, in cycle 1 to cycle 2 it increased from 66% to 84%. For the drafting, in cycle 1 to cycle 2, from 56% increased to 75%. For the revising, in cycle 1 to cycle 2, from 47% increased to 72%. And for the editing, in cycle 1 to cycle 2, from 63% increased to 81%.
FILTERING OF SIGNALS WITH UNKNOWN INTERFERENCE POWER AND RANDOM JUMPING PERTURBATION

A linear stochastic dynamic system under the influence of random disturbances and interference is considered. The perturbation is a sequence of uncorrelated random variables with a distribution in the range [–1, 1]. This sequence is modeled by a random binary signal with values 1 and 0 and is described by a Markov chain with known probabilities of transitions from one state to another. The modulated signal is fed to the input of the linear control unit. The output signal of the control object is measured with an error, which is a sequence of uncorrelated random variables with an unknown distribution in the range [–1, 1].The problem under consideration differs from the optimal linear filtration problems based on the application of the Kalman filter and its modifications. Its novelty consists in the following: 1) the input signal is a random jumping process – uncorrelated noise modulated by a random binary signal; 2) the variances of random processes – the input signal and the interference characterizing their power are unknown. А posteriori mathematical expectation and the a posteriori variance of the filtering error are determined by the methods of Bayesian estimation and the theory of systems with a random jump structure. The optimal estimation algorithm is described by a system of recurrent equations. It consists of five interconnected blocks: 1) a meter of mathematical expectation and dispersion of an additive mixture of an output signal with interference; 2) an indicator of a random structure; 3) a classifier of a random structure; 4) a dispersiometer; 5) a filter.
Changes in the structure of programs for the training of scientific and scientific-pedagogical personnel in graduate school, taking into account federal state requirements

The problem of training scientific and scientific-pedagogical personnel for modern society is one of the key problems limiting the growth of the personnel reserve for educational and scientific institutions. The article presents the main changes in the training of highly qualified personnel implemented on the basis of federal state requirements for the structure of programs for the training of scientific and scientific-pedagogical personnel in graduate school on the basis of the Moscow State Academy of Veterinary Medicine and Biotechnology – MVA by K. I. Skryabin, which will contribute to the timely defense of dissertations and increase the performance indicators of the graduate school.
Two-layer genetic programming

This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.
Effects of conditioned losses in two-photon quantum walks

We experimentally study the impact of conditioned losses on the evolution of an integrated two-photon quantum walk. Well-localized losses change the output distribution of the walk, but do not alter its asymptotic ballistic spreading.
Ontological and Epistemological Aspects of Modeling: Modelling Relation and Adiaphoric Systems

&lt;p&gt;Based on the consideration of the modelling relation proposed by R. Rosen, the article presents ontological and epistemological aspects of modeling causal system processes. The modelling relation makes it possible to compare systems and their models, and also allows to proceed to the definition and well-known formalization of measurement processes. The considered ontological aspect of modeling includes the concept of complex systems and, in the case of impredicative systems, allows us to speak about the allocation of a qualitatively more complex class of supercomplex systems. In epistemological terms, based on the provisions of transcendental psychology, the definition of generating (form-generating) systems is given as adiaphoric systems, whose mechanisms, as a rule, do not manifest themselves in generated forms and products. The adiaphoric nature of the processes under study does not directly depend on the ontological complexity of the modeled system and means that the system generates new structures and forms, regardless of any manifested properties or produced parts and products. Adiaphoric systems can take place when considering the processes of transition between backbone form-generating structures studied by various hierarchically interconnected sciences.&lt;/p&gt;
The Possibility of Generating Photon Cluster using Ions in a Linear Trap

Application of Effective Hamiltonian theory on trapped ions in thermal motion proves it possible to generate Photon Cluster States using linearly trapped ions through bichromatic-interaction if implementing quadratic stark shift on trapped ions is achievable.
Extremely Randomized Tree Based Sentiment Polarity Classification on Online Product Reviews

The sentiment analysis of user reviews on social networking is one among the fundamental process carried out by online business organizations to improve the quality of their products and retain the customers and thereby lift the monetary benefit. Although numerous analysis models exist, still there is space for improving the performance and accuracy of informal text data-based classification models. In this examination, we conduct a comparative assessment of the effectiveness of unigram feature set extricated utilizing n-gram technique with three ensemble methods namely Extremely Randomized Tree, Voting, and Bagging classifier based on the following five baseline classifiers Random Forest, Naïve Bayes, K-NearestNeighbor, Ridge Classifier, and Support Vector Machine to identify polarity from mobile product reviews. Among the three ensemble methods, the Extremely Randomized Tree technique has better outcomes with the accuracy of 98% for positive and 85% for negative cases, with an overall accuracy of 96.8%. The error rate of all the three ensemble classifiers is also under 0.5% which uncovers that ensemble classifiers performs better compared to individual classifiers.
CHANGING APPROACHES TO TEACHING STUDENTS MAJORING IN «TRADE BUSINESS» IN CONDITIONS OF DIGITAL MARKET TRANSFORMATION

The article formulates reasons for the need to change approaches to the teaching students in the conditions of digital transformation of the economic system. The authors identified the advantages and disadvantages of involving employers’ representatives in the organization of the educational process. The results of a survey of students studying at the master’s program «Ecosystem of International Trade» and majoring in «Trade business» concerning reasons for choosing the direction of study and the scientific interests of students are presented. In conclusion, the issues of teaching students in modern conditions are considered
A Study to Evaluate the Effectiveness of Google Solution on Middle School Teacher and Student Based on the Kirkpatrick 4 Level Model

The purpose of this study is to evaluate the effectiveness of Google Solution (GS) on the application of classes. Participants were 17 teachers and 115 students of 'O' middle school in Busan, and the period was from April to September for five months. As a treatment, each teacher was given a Chromebook equipped with Google
Optical flow estimation based on local-global modeling and visual similarity guidance

Optical flow estimation is a research core of computer vision. In recent years, optical flow estimation methods based on convolutional neural networks (CNNs) have achieved great success. However, most of the existing CNN-based methods are incapable of modeling long-distance dependencies successfully due to the limited receptive fields of convolutions. This may lead to poor performance of optical flow estimation in regions of large displacements and local ambiguities. Furthermore, the problem of edge-blurring remains an open challenge for CNN-based optical flow methods because the general interpolation operation tends to amplify the optical flow errors during the upsampling process. To deal with the abovementioned problems, this paper proposes a novel optical flow estimation method based on local-global modeling and visual similarity guidance. First, an efficient and simple self-attention module is adopted to improve the local-global modeling ability of the network and to extract the more representative features, which decreases the number of optical flow errors caused by large displacements. Second, based on the assumption that the visual features of the objects are more similar, the motion is more similar, and a visual similarity-guided optical flow upsampling network is constructed to guide the upsampling process. By transforming the visual similarity of features into motion similarity, the proposed upsampling scheme improves the accuracy of optical flow estimation in regions of motion boundaries. Finally, we run the proposed method on MPI-Sintel and KITTI test datasets to conduct a comprehensive comparison with some state-of-the-art methods. The experimental results indicate that the proposed method achieves top performance among the comparable methods and, in particular, gains significant developments in regions of large displacements and motion boundaries.
A survey of the effect of education based on planning behavior theory on increasing physical activity in elderly women

A survey of the effect of education based on planning behavior theory on increasing physical activity in elderly women
Tourist Behavior-based Early Warning Scheme for Wisdom Tourisms

In this paper, we propose a warning scheme for behavior early warning in scenic spots.In the scheme, it determines whether to send an alarm reminder based on the current location of the visitor's mobile terminal.The warning scheme includes obtaining the warning location information based on preset dangerous location data, as well as obtaining the current location information of the visitor at a preset time interval, and setting the current location of the visitor.The location information is compared with the warning location information, and if the comparison results are consistent, a virtual alarm operation is triggered.By comparing the current location of the tourist with the warning location information, it is determined whether the virtual warning operation is triggered, so that tourists can obtain real-time warning information in the scenic spot, for reducing the probability of uncivilized or dangerous behaviors in the scenic spot, improving the warning operation, and making sure that it is safe and civilized to travel in the end.
Non-Gaussian entanglement criteria for atomic homodyne detection

Homodyne measurement is a crucial tool widely used to address continuous variables for bosonic quantum systems. While an ideal homodyne detection provides a powerful analysis, e.g. to effectively measure quadrature amplitudes of light in quantum optics, it relies on the use of a strong reference field, the so-called local oscillator typically in a coherent state. Such a strong coherent local oscillator may not be readily available particularly for a massive quantum system like Bose-Einstein condensate (BEC), posing a substantial challenge in dealing with continuous variables appropriately. It is necessary to establish a practical framework that includes the effects of non-ideal local oscillators for a rigorous assessment of various quantum tests and applications. We here develop entanglement criteria beyond Gaussian regime applicable for this realistic homodyne measurement that do not require assumptions on the state of local oscillators. We discuss the working conditions of homodyne detection to effectively detect non-Gaussian quantum entanglement under various states of local oscillators.
Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles

We present a joint camera and radar approach to enable autonomous vehicles to understand and react to human gestures in everyday traffic. Initially, we process the radar data with a PointNet followed by a spatio-temporal multilayer perceptron (stMLP). Independently, the human body pose is extracted from the camera frame and processed with a separate stMLP network. We propose a fusion neural network for both modalities, including an auxiliary loss for each modality. In our experiments with a collected dataset, we show the advantages of gesture recognition with two modalities. Motivated by adverse weather conditions, we also demonstrate promising performance when one of the sensors lacks functionality.
Big Book Media in Early Reading Learning of First Grade Students

Curently students experience difficulties in reading at school, moreover there is no learning media used by teachers to support initial reading skills in the learning process in grade 1 due to limited facilities and infrastructure at school. The purpose of this study is to develop and find out the validity of the big book media in learning to read at the beginning of grade 1 elementary school. While the subjects in this study were 1 teacher, 2 students and 2 design aspects, 2 material aspects, and 2 language aspects. The methods used to collect data are observation, interviews, and questionnaires. The instruments used were validation sheets and interview sheets and the data analysis techniques used were quantitative and qualitative. The research results obtained are big book media on theme 1 sub-theme 1 for grade 1 elementary school students who are suitable for use based on the assessment of design experts with a percentage of 93.8% with valid criteria, linguists 94.4% with valid criteria and material experts with a percentage 86.3% with very valid criteria. The average percentage of validation results is 91.5% with a very valid category without revision.
An Action Research Study on Applying Mobile-App-Based English Activities to Develop 26 4th Graders’ Interest in English Learning

Objectives In this study, for the purpose of improving 26 4th graders’ English learning interest, a homeroom teacher conducted an action research study and helped their mobile-app-based English learning and her adviser supervised the teacher to conduct the research study successfully, according to qualitative research tradition. Although many theorists and scholars propose promising effects of mobile-app-based English learning there is not much mobile-app-based English learning research conducted with primary school students and teachers at public primary school settings. This is an action research study to fill the gap in the previous studies.&#x0D; Methods During the spring semester of 2022, 26 elementary school students in a southern province performed mobile-app-based English learning activities with their homeroom teacher in a 30-minute morning-class session and in a 40-minute club activity session. Teacher observation reports, survey results and interview data are collected before, in or after the mobile-app-based English teaching/learning class.&#x0D; Results The first result is that almost all of the 26 students liked the English learning activity of singing English songs using YouTube app. As for the second result, among the four English learning apps applied in the study, most of the students liked the Dino Buddies series of the Little Fox app and the Kahoot word quiz app the most, and low-English-learning-autonomy students liked the ‘FunEasyLearn’ English app the most. The third result is that all 26 students' interest in learning English improved while participating in the study, with varying degrees of improvement. The fourth result is that the preference for mobile-app-based English learning activities varies greatly by student according to the level of English-learning interest improvement.&#x0D; Conclusions The results of the study showed that 26 students who conducted English learning using mobile apps for one semester developed interests in learning English and that they all had various preferences for mobile-app-based English learning activities. Based on the study results, it can be predicted that the learning effect will be maximized if mobile-app-based English teaching is carefully planned and implemented according to the characteristics, autonomy, personality, and English proficiency levle of each student as a learner. In particular, when carrying out mobile-app-based English teaching for students with low autonomy, it would be very helpful if an individual learning helper or an assistant teacher helps their learning. It seems that further research is still needed to know how to implement mobile-app-based primary English education for Korean learners.
How to Become a Software Company

Across industries, companies strive to become software companies. Becoming a software company, however, means more than doing software. It is about being software. It is a culture change that needs much more than window dressing by marketing. Joachim Fetzer and I present experiences of how to become a software company. A Marelli case study emphasizes the transformation with practical insights. I look forward to hearing from you about this column and the technologies that matter most for your work.–Christof Ebert.
The DetectDeviatingCells algorithm was a useful addition to the toolkit for cellwise error detection in observational data

ObjectivesWe evaluated the error detection performance of the DetectDeviatingCells (DDC) algorithm which flags data anomalies at observation (casewise) and variable (cellwise) level in continuous variables. We compared its performance to other approaches in a simulated dataset.Study Design and SettingWe simulated height and weight data for hypothetical individuals aged 2–20 years. We changed a proportion of height values according to predetermined error patterns. We applied the DDC algorithm and other error-detection approaches (descriptive statistics, plots, fixed-threshold rules, classic, and robust Mahalanobis distance) and we compared error detection performance with sensitivity, specificity, likelihood ratios, predictive values, and receiver operating characteristic (ROC) curves.ResultsAt our chosen thresholds error detection specificity was excellent across all scenarios for all methods and sensitivity was higher for multivariable and robust methods. The DDC algorithm performance was similar to other robust multivariable methods. Analysis of ROC curves suggested that all methods had comparable performance for gross errors (e.g., wrong measurement unit), but the DDC algorithm outperformed the others for more complex error patterns (e.g., transcription errors that are still plausible, although extreme).ConclusionsThe DDC algorithm has the potential to improve error detection processes for observational data.