Reviewerly/exHarmony huggingface dataset:
Cancer Cell Growth Discovery Utilizing Computerized Image Processing Strategies

Starting late, the image getting ready segments are used for the most part in a couple of correction areas to improve the earlier stages of dissemination and medication, where the time factor is essential to discover the patient’s disease as much as possible, exclusively in various cancerous tumors like lung cancer. In recent years in lung cancer, the idea of treatment and sciatica systems has been associated with uncomfortable medication. The 2008 findings suggest that lung cancer attacks most people worldwide. Early revelation of lung cancer is critical for productive medication. There are very few systems open to perceive cancerous cells.
Kaitan Pembelajaran Bahasa Indonesia dengan Lingkungan

The environment is a place where many people interact. The environment is an early part of human life, how a person's language development starts from the environment. Good or bad humans can be judged from their environment. In everyday life situations, a polite attitude will have a positive impact on social relations with the surrounding environment (Gani, 2019).
Characteristics Environmental and Anopheles Larva Species In High And Low Clinical Malaria Cases In The Landak District of West Kalimantan Province

Malaria remains a health problem in Indonesia. West Kalimantan is a malaria endemic area with high and lowincidence. Landak District is one of the malaria endemic area. Malaria cases were found in the areas around illegalgold mining and oil palm plantations. The aims of this study were to describe the characteristics of the breeding sitesand species of Anopheles larvae found in high malaria cases area, namely Amboyo Utara Village and low clinicalmalaria cases, area namely Mandor Village. This research is a descriptive research with cross sectional design. Thesamples were Anopheles larvae collected with Accidental sampling technique in the breeding sites. Environmentalcharacterization of breeding sites were physical characteristic including water temperature and sun exposure,chemical characteristic including water pH and salinity, and biological characteristics including water biota. Theresults of this study ware environmental characteristics that have the potential to breed Anopheles mosquitoes inAmboyo Utara Village, including water temperature 26-30C, shandy, water pH 5.0-7.6, salinity 0.2-1.0 ppt, biotaswater hyacinth, grass and tadpole. The Mandor village, water temperature 29-30 ºC, shandy, pH of 6.9-8.0, salinity of0.5 ppt, water biota grass. Anopheles species found in Amboyo Utara village were larvae of An. vagus (94.30%), An.tessellatus (3.42%), An. subpictus (1.62%), An. indefinitus (0.81%) and An. maculatus (0.81%). Characteristics ofbreeding sites in Mandor village were larvae of An. maculatus (11.11%), An. subpictus (3.70%), and An. vagus(85.18%). The conclusion of this study was that di erent species found at breeding sites with di erent environmentalcharacteristics in both high and low malaria areas in Landak District, West Kalimantan Province.
Correction

A clarification on the SHINE X-ray free-electron laser mentioned in the article “Big science booms in China” in the Physics World Big Science Briefing.
Henry David Thoreau

In Henry David Thoreau’s most famous works, a natural world overwhelmed by humanity’s actions seems beyond the author’s imagination, as he frequently portrays nature as either outwitting humanity or as sublimely invulnerable. However, recent scholarship on Thoreau has turned increasingly to the more scientifically focused works from the close of his career in order to temper his optimistic self-representation. Thoreau’s later passions for mapping, cataloguing, and interacting with material objects that straddle the natural and human worlds present a literary persona more useful to current Anthropocene scholarship and environmental activism than the seemingly overconfident philosophical voice of his early career.
Identification and Control of Multidimensional Delayed Processes in Conditions of Incomplete Information

Problems of identification and control of multidimensional discrete-continuous processes with delay in conditions of incomplete information about the object are considered. In such conditions, the form of parametric equations for various channels of the object is absent due to the lack of a priori information. Moreover, multidimensional processes have stochastic dependences of the components of the vector of output variables. Under such conditions, the mathematical description of such processes leads to a system of implicit equations. Nonparametric identification and control algorithms for multidimensional systems are proposed. The main task of modeling such processes is to determine the predicted values of the output variables from the known input. Moreover, for implicit equations, it is only known that one or another output variable can depend on other input and output variables that determine the state of a multidimensional system. In this study, a nontrivial situation arises when solving a system of implicit equations under conditions when the dependences between the components of the output variables are unknown. The application of the parametric theory of identification in this case will not lead to success. One of the possible directions is the use of the theory of nonparametric systems. The main content of the work is the solution of the identification problem in the presence of dependencies of the output variables and then the solution of the control problem for such a process. Here you should pay attention to the fact that when determining the reference actions for a multidimensional system, it is first necessary to solve the system of reference actions, since it is not possible to choose arbitrarily setting influences from the range of definition of output variables. Computational eXperiments aimed at investigating the effectiveness of the proposed identification and control algorithms are presented.
Modeling Relationships Between Feature Model Views

The development of high-tech systems involves several models and artifacts, each focusing on one or more aspects or parts of the system. In product lines of such systems, managing the common and variable characteristics of these development artifacts by means of feature models gets complex due to the large number of features and constraints. Using multiple feature models, each with reduced number of features relevant only for specific artifacts, has been identified as a possible solution to deal with this complexity. However, the advantages of explicit representation of relationships between multiple feature models remain unexploited. These relationships need to be represented and managed explicitly in order to improve understanding of the complete variability model and support maintainability of the variability model under evolution. In this paper, we propose a modeling language that can be overlaid, non-intrusively, on existing multiple feature models to express the relationships between feature models. The language uses the concept of views to represent different aspects of the product line variability model, each view corresponding to a feature model represents variability information in one or more development artifacts of the product line. We give a metamodel for relationships between feature model views and provide the semantics of the relationships. We present how proposed relationship types can be used to analyze the impact of a change on the complete variability model. Furthermore, we provide formal definitions of relationship types, making them suitable for existing automated analysis operations in the literature.
An improved RRT dual tree path planning algorithm

Aiming at the problems of high randomness, many redundant nodes and low efficiency when the map is complex, an improved RRT dual tree algorithm is proposed. Firstly, the algorithm adopts the method of bi-directional growth and adding the target bias factor to the starting point expansion tree to shorten the running time of the algorithm and reduce the probability of exploring the invalid space; secondly, it simplifies the path by greedy thought, removes the redundant nodes, and smoothes the corner by quadratic B-spline curve; finally, through the comparison of simulation experiments. The algorithm has higher search efficiency and better results ,the algorithm has shorter path and higher smoothness, which verifies the accuracy and feasibility of the algorithm.
Fourier analysis

Any periodic sound, as well as any other periodic oscillation of any complexity, can always be represented as a superposition of sinusoidal components with frequencies that are all multiples of the fundamental frequency of the oscillation, and with specific amplitudes. Each of these components is termed a Fourier partial, harmonic, or overtone component. A Fourier spectrum is a bar graph of amplitude versus frequency, in which all the partials are represented. The goal of Fourier sound analysis is to identify all the partials present in a sound, and their relative amplitudes. Fourier synthesis, instead, is a method used to electronically construct a signal with a specific, desired periodic waveform, by combining sine waves, which is not so interesting for reproducing the sound of real musical instruments, but excellent to create new and exciting sounds. Since both human hearing and Fourier spectra are sensitive to frequency and amplitude, but not to phase or waveform, human hearing, including ear and brain, is a Fourier analyzer.
The possibility of superradiation without the Penrose process

In the traditional theory, it is impossible to produce superradiation in a straight time and space without a Penrose process. We know that similar entities can be artificially created by formulas that exist in the algebraic field. We have found that when a certain potential is created, there will be a superradiation phenomenon without the Penrose process in flat space and time.
Evaluation model of teaching effectiveness of sports characteristic course based on intelligent fuzzy system

In order to improve the accuracy of the teaching effect evaluation, a teaching effect evaluation model based on the intelligent fuzzy system is designed. The evaluation index are selected based on the teaching situation of physical education courses, relevant national policy documents, subject textbooks, intelligent fuzzy system to modify the index system through expert interview, determine the weight coefficient of each index by hierarchical analysis method (AHP), and calculate the single layer and total ranking of the index matrix to realize the evaluation of physical education courses. The test results show that the fuzzy evaluation accuracy of the proposed model is above 95.63%, with high evaluation performance and strong utility.
Vladikavkaz Loris-Melikov Vocational School

Mikhail Tarielovich Loris-Melikov is a person who has gone down in the history of Vladikavkaz by many good deeds that have left a rich cultural and historical mark forever. He took the most active part, including financially, in the organization and functioning of a number of charitable and social and cultural institutions, donating significant sums from his own capital and income. The craft school of pre-revolutionary Vladikavkaz survived all epochs of political transformations and military events. Thanks to the vocational school, production on the territory of Ossetia developed successfully since there were high-quality trained cadres in a new direction, but functions to this day, preserving the memory of Mikhail Tarielovich as its founder. The financial investment and the capital left by Loris-Melikov in his will served as the basis for the functioning of the school during the first few decades since its opening. The difficult periods in which the educational authorities were forced to change the rules of admission in connection with those and other reasons causing financial difficulties are also briefly touched upon. The article provides information on income in the form of interest deducted by the bank and the expenses of these amounts for the needs of the school, including scholarships for students. The content of the publication will make it possible to trace the expansion of directions in the training of specialists due to the opening in different years of new departments, with a narrow specificity. The publication will allow you to have an idea of the urban life of this period, since it partly reflects the life of the inhabitants who turned to the workshops of the vocational school for household services, which the school was quite capable of providing conclusion, we are talking about the post-revolutionary activities of the school based already on the Soviet education system.
Analysing the Correlation between Convolutional Filter Sizes and Classification Accuracy Using MNIST

Handwriting recognition is the process of automatically converting handwritten text into electronic text (letter codes) usable by a computer. The increase in technology reliance during an international pandemic caused by COVID-19 has showcased the importance of ensuring the information stored and digitised is done accurately and efficiently. Interpreting handwriting remains complex for both humans and computers due to the various styles and skewed characters. In this study, we conducted a correlational analysis on the association between filter sizes and the convolutional neural networks (CNN’s) classification accuracy. The testing has been conducted from the publicly available MNIST database of handwritten digits (LeCun and Cortes, 2010). The dataset consists of a training set (N=60,000) and a testing set (N=10,000). Using ANOVA, our results indicate a strong correlation (.000,P≤0.05) between filter size and classification accuracy. However, this significance is only present when increasing the filter size from 1x1 to 2x2. Larger filter sizes were insignificant therefore, a filter size above 2x2 cannot be recommended.
A <i>m</i>Θ analysis and a decoding <i>α</i>-Viterbi algorithm of the <i>m</i>Θ convolutional codes on

The notions of mΘ codes, mΘ linear codes and mΘ cyclic codes are defined in [4; 5; 6]. The notion of mΘ convolutional code on the mΘ field is defined in [8]. In this note, we plan to propose a decoding process of a mΘ convolutional code on the mΘ field .
Efficient Delivery Of Government Schemes Using Blockchain Technology And Cryptography

India is a country of growing population and agriculture is the primary source of livelihood for about 58 per cent of Indians population. The Indian food industry is poised for huge growth, increasing its contribution to world food trade every year due to its immense potential for value addition, particularly within the food processing industry. Government of India is giving more priority for welfare of the farmers. In this regard it is implementing several welfare schemes to provide financial support to farmers. Various schemes were introduced by the government, but the benefits of these schemes do not reach to the common people in our case farmers due to corruption happening at the middleware. Hence there is need of a reliable, authorized and secured system to maintain and manage these transactional records. This is where blockchain comes into picture. One of the aims of our system is to ensure that the amount transferred by the authority to a particular farmer, reaches to him/her without any corruption and system manipulation using the features of blockchain and cryptography.
Private capital inflows, institutions and economic growth in Africa

The study examines the relationship between private capital inflows (foreign direct investment and remittances), institutions and economic growth in a panel of 45 African countries from 2002 to 2018. Using the system generalized method of moments (SGMM), the study found that foreign direct investment inflows (FDI) have a significant and positive impact on economic growth in Africa. However, remittances exert no significant impact on economic growth. The study further analyzed the moderating role of institutions in the relationship between private capital inflows and economic growth by examining the impact of the interactions between private capital inflows and institutions on economic growth. The study concluded that the coefficient(s) of the interplay between FDI and institutional variables is negative and statistically significant presupposing that institutions in Africa do not complement FDI inflows in driving economic growth. In the same vein, the coefficient of the interactive variables between remittances and institutions (regulatory requirement) is negative and significant indicating that remittances and institutions are substitutes rather than complements. The implication of the study, therefore, lies in the fact that institutions in Africa are weak and as a result cannot play a supportive role in reaping the positive spillovers of private capital flows to Africa.
NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.
Developing a Dialogic Space for Moving towards Critical Multicultural Education

This chapter builds on a cooperative self-study project that involved 14 university-based teacher educators in the School of Education at the University of Iceland. Over three years, they engaged in self-study to understand how dialogue could aid their understandings of how their cultural backgrounds influence their work as teacher educators. Specifically, they worked to become aware of their identities and educational practices, developing a critical understanding of how they can either resist or reify existing structures of inequity. The purpose of this collective self-study was to document and analyze the process of developing a discursive space that 14 university-based teacher educators in Iceland co-designed and negotiated to mobilize the diverse understandings and experiences of the group to work towards multicultural teacher education. The aim was to strengthen teacher education through a “diversity-as-asset lens.” Their research question was: What did the co-authors, Karen and Gunnhildur, learn through their negotiation with each other and their colleagues in the process of creating a discursive space for moving towards multicultural teacher education? The data collected included focus group interviews, self-interviews, and audio-recordings of meetings. They used art-based analysis methods via the co-construction of sculptures and poems to create a dialogic space (Freire, 1970). Doing so led teacher educators to develop a shared agenda for collective transformation. Ultimately, this inquiry led them to collectively name and interrogate ideas and identities to transform their understandings and practices as teacher educators, helping them (re)commit to educating teachers in ways that prioritize equity and justice (Kitchen et al., 2016; Zeichner, 2018).
The Reve’s Puzzle Revisited

The Reve’s puzzle, introduced by the English puzzlist, H.E. Dudeney, is a mathematical puzzle with 10 discs of different sizes and four pegs, designated as S, P1, P2 and D. Initially, the n (  1) discs rest on the source peg, S, in a tower (with the largest disc at the bottom and the smallest disc at the top). The objective is to move the tower from the peg S to the destination peg D, in a minimum number of moves, under the condition that each move can transfer only one disc from one peg to another such that no disc can ever be placed on top of a smaller one. This paper considers the solution of the dynamic programming equation corresponding to the Reve’s puzzle.
The Moderating Impact of Team Tenure on Team Trust - Team Performance Relationship with Relevance to Executive Level Employees of Sri Lankan Tyre Manufacturing Industry

Even though, many determinants of team performance are found in extended literature, team trust and the moderating effect of team tenure is little investigated in Sri Lankan context, and it has not been studied yet. Bridging the gap in this context, the current study assessed the moderating impact of team tenure on team trust and team performance relationship in Sri Lankan tyre manufacturing industry. The current study was conducted as a cross-sectional study among a sample of one hundred and ninety-two executive level employees selected from four major tyre manufacturing organizations following the stratified random sampling technique. Primary data were collected using a standard questionnaire distributed via Google form. The collected data were analysed with the support the SPSS employing correlation, regression, descriptive statistics and process matrix. It is found that team tenure does not moderate the relationship between team trust and team performance, and also founded that strong positive relationship between team trust and team performance. Moreover, team trust impact positively on team performance and team tenure does not impact team trust and team performance separately. It is recommended to tyre manufacturing organizations to facilitate more open communication and information sharing in order to improve executive level employees trust within teams, which in turn, will affect their team performance. Future researchers can use other variables like organizational support, team diversity, and personality as moderators of their study.
Federated Learning for Recognizing Private Handwritten Numbers

Federated learning (FL) is a new technology in the field of machine learning, which trains an algorithm across multiple decentralized edge devices or servers holding data samples, without exchanging their data samples. Horizontal Federated Learning (HFL) is a FL method focusing on using the same feature space across all users, which is suitable for situations where users’ data feature dimensions overlap more. Because FL has the advantage of privacy protection in the field of manufacturing, we propose an HFL-based deep learning method named FedCNN, which realizes the digits handwritten recognition without violating the privacy or data sharing. Three different methods (local model, centralized model, and HFL-based FedCNN) are used on a public data set (MINIST) to compare the pros and cons of the solutions. The experimental results demonstrate that FedCNN scheme can simultaneously achieve practical privacy preservation and good model generalization performance with Homomorphic Encryption.