Reviewerly/exHarmony huggingface dataset:
Social Learning Optimization

This chapter discusses the novel optimization approach that mimics the social learning process of humans. It describes a novel algorithm from the perspective of mimicking the social learning process of humans, which was first developed by Gong et al. in 2014. According to the social learning theory, the observational learning process consists of four basic steps: attention, retention, reproduction, and motivation. The experiments and results in this chapter will validate the good performance of social learning optimization (SLO), in terms of global search ability, local exploitation, and efficiency. Based on this observation, a novel optimization technique termed the SLO has been developed, which emulates the social intelligence of humans in computers. The attention symbol will be used to guild the subsequent reproduction and reinforcement procedures. The main process includes four basic operators, i.e., attention, reproduction, reinforcement, and motivation, based on which the society converges to the optimum of the problem at hand.
Image Steganography using 3 ways of Encryption for Highly Secured Data Transmission

Steganography exists from many years in a variety of forms and has been used in a variety of domains. There are various types of steganographic techniques that are used to hide data in various file formats. The proposed model uses three layers of security using three algorithms namely, LSB, DES and AES. The software allows the user to encrypt the secret message which gets protected by undergoing three layers of encryption. The output of the encryption process which is the steno image can be sent across through any medium along with the secret key to decrypt. The end user then uses a unique key to extract and decrypt the secret message from the image. There are many reasons to hide data but the simplest is the need to prevent unauthorized persons from accessing the secret information.
KEMAMPUAN MENULIS PUISI DENGAN TEKNIK KATA KUNCI PADA SISWA KELAS X SEMESTER GANJIL SMK TRISAKTI BANDAR LAMPUNG TAHUN PELAJARAN 2018/2019

This study discusses ability to write poetry with keyword techniques in the odd semester X class X students of SMK TRISAKTI Bandar Lampung 2018/2019 academic year. The focus of the study studied consisted of the suitability of the content of the poem with the specified theme, poetry can reveal the mandate, the suitability of the choice of words or diction with the predetermined theme and the word chosen as the keyword. This study uses a qualitative method. Measuring the ability of students to write poetry is measured by a written test. This study took a sample of one class X AK 1 Bandar Lampung SMK TRISAKTI 2018/2019. The technique used is the technique of collecting data using the main techniques, and supporting techniques. Research instruments using assignments in writing, as well as qualitative data analysis techniques. The ability of students to write poetry with indicators of theme suitability, reveals the mandate of choice of words, majas and pengimajian or imagery can be concluded that there are still students who find it difficult to write poetry with keyword techniques.
Best Practices for the Use of Desk Rejects

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Estimation of the complexity of constructing a logical classification tree for an arbitrary case in conditions of strong class separation of the initial training sample

The paper offers an estimation of the complexity of the constructed logical tree structure for classifying an arbitrary case in the conditions of a strong class division of the initial training sample. The principal solution to this question is of a defining nature, regarding the assessment of the structural complexity of classification models (in the form of tree-like structures of LCT/ACT) of discrete objects for a wide range of applied classification and recognition problems in terms of developing promising schemes and methods for their final optimization (minimization) of post-pruning structure. The presented research is relevant not only for constructions (structures) of logical classification trees, but also allows us to extend the scheme of complexity estimation to the General case of algorithmic structures (ACT models) of classification trees (the concept of algorithm trees and trees of generalized features - TGF). Is investigated the actual question of the concept of decision trees (tree recognition) – evaluation of the maximum complexity of the General scheme of constructing a logical tree based classification procedure of stepwise selection of sets of elementary features (they can be diverse sets and combinations) that for given initial training sample (array of discrete information) builds a tree structure (classification model), from a set of elementary features (basic attributes) are estimated at each stage of the scheme of the model in this sample for the case of strong separation of classes. Modern information systems and technologies based on mathematical approaches (models) of pattern recognition (structures of logical and algorithmic classification trees) are widely used in socio-economic, environmental and other systems of primary analysis and processing of large amounts of information, and this is due to the fact that this approach allows you to eliminate a set of existing disadvantages of well-known classical methods, schemes and achieve a fundamentally new result. The research is devoted to the problems of classification tree models (decision trees), and offers an assessment of the complexity of logical tree structures (classification tree models), which consist of selected and ranked sets of elementary features (individual features and their combinations) built on the basis of the General concept of branched feature selection. This method, when forming the current vertex of the logical tree (node), provides the selection of the most informative (qualitative) elementary features from the source set. This approach allows you to significantly reduce the size and complexity of the tree (the total number of branches and tiers of the structure) and improve the quality of its subsequent instrumental analysis (the final decomposition of the model).
A Step Towards Explainable Person Re-identification Rankings

More and more video and image data is available to security authorities that can help solve crimes. Since manual analysis is time-consuming, algorithms are needed that support e.g. re-identification of persons. However, person re-identification approaches solely output image rank lists but do not provide an explanation for the results.In this work, two concepts are proposed to explain person re-identification rankings and a qualitative evaluation is conducted. Both approaches are based on a multi-task convolutional neural network which outputs feature vectors for person re-identification and simultaneously recognizes a person’s semantic attributes. Analyses of the learned weights and the outputs of the attribute classifier are used to generate the explanations. The results of the conducted experiments indicate that both approaches are suitable to improve the comprehensibility of person re-identification rankings.
ezNL2SQL: A System for Network Devices Management with a Natural Language Interface for Databases

To enhance and accelerate the work of network management experts of large IT environments, we present ezNL2SQL. It is a fast and flexible system that provides a natural language interface to a database in a domain-specific setting, where column and table names do not correspond to their underlying entities and a large labelled corpus is not available. Our system comprises three main components: linguistic index, natural language query analyzer and SQL generator, and an easy-to-use UI. Given an input question and a target database, ezNL2SQL generates a valid SQL statement for querying data that best satisfies the information request specified by the question. The result is presented to the user in the most suitable visual format. We demonstrate the usefulness and efficiency of the system in a user study with network management experts and a complex network security inventory database.
Verbalization vs visualization: the issues of interaction and communication

the article contrasts two technologies of distant education and scientific activity: visual (presentations) and verbal (by correspondence, Newtonian). The advantages and disadvantages of both technologies are considered, and a preference is given to writing aimed at thinking.
Generic Association Rule Engine Optimization Using Item Support Projection

Transactional data analysis and association rule learning have become prominent requirements for effective decision making in marketing operations. Considering that, we propose an optimized near-real-time item associations generation mechanism combined with database utilities. Our solution adopts the association measures of the Apriori algorithm and constructs a live item associations database fed by a generic rule engine. The study also covers redundancy avoidance, memory management and rare event handling with logarithmic transformation. With an experimental case study, we demonstrate hyper-parameter tuning methods and evaluate the results for various scenarios.
MAIN STAGES OF STUDENT RESEARCH ACTIVITIES IN HIGHER EDUCATIONAL INSTITUTIONS

This article describes the main stages of student research in higher education institutions.
Effects of Woo-Ri Dance Exercise Program for 16 Weeks on Cognitive Function and Senior Fitness test in Elderly Women

The purpose of this study was to investigate how Woo-Ri dance exercise program affects the cognitive function and senior fitness in elderly women. The subjects of this study were 30 female senior citizens aged 65 or older in the metropolitan city of B, and were classified into two groups: the experimental group Woo-Ri dance exercise(n=15) and the control group(n=15). The experimental group was conducted three times a week for 16 weeks. The data were calculated using the SPSS(24.0) statistics program to calculate the mean value(M) and standard deviation(SD) for each variable. To investigate main effects and interaction effects, you performed a two-way iterative ANOVA and set the post-comparison with the significance level to less than 0.05. The result was a significant increase in the main effect(p<.001) of Woo-Ri dance exercise on changes in K-MMSE as a marker of cognitive functions, and in the case of controls, the main effect was significantly reduced and the interaction effect(p<.001). For six functional fitness tests, the main effects of Woo-Ri dance exercise(p<.01, p<.001) was shown respectively, and in the control group, the main effects(p<.05, p<.01, p<.001) The interaction effects (p<.001) with were shown. Based on the results of the study, Woo-Ri dance exercise improves muscle strength, flexibility, balance, agility, cardiopulmonary endurance, and cognitive function. Therefore, Woo-Ri dance exercise is considered to be a safe and effective exercise that does not strain our joints, and will use the lives of elderly women as a physical, mental and social healthy program.
Research of senior high school students’ economic culture formation in the conditions of development of the digital education environment

The aim of the work is to study, analyze the state and model of the process senior high school students’ economic culture formation Materials and methods. The main research methods are theoretical analysis of scientific, psychological and pedagogical literature on the research problem, study and generalization of experience on the problem; observation, questioning, conversation, qualimetry, testing, method of expert assessments; ascertaining and formative experiment; analysis of the study; methods for mathematical statistical processing, statistical processing empirical data. The study was conducted on the basis of Krasnoyarsk State Pedagogical University named after V.P. Astafiev with bachelor’s degree and master’s degree students, high school students, teachers from educational institutions, students of universities and secondary professional educational institutions of Krasnoyarsk and Krasnoyarsk Territory. The obtained dependencies and research data made it possible to form a model of the senior high school students’ economic formation in the context of the development of the digital educational environment of the region, describing the degree of formation of each component at each level.
Developing Ontologies for Adaptive Collaboration in a Dynamic Environment

Constructing or modelling new knowledge-based systems is common while developing new knowledge-based systems. Current knowledge modelling approaches tend to concentrate on the subsystem, considering domain knowledge to be highly dependent on the particular task at hand. However, the importance of task-independent knowledge bases (or "ontologies") for large-scale integration has recently been highlighted in a variety of ways.This article looks at three different approaches to creating ontologies and utilises one of them to create an ontology for adaptive collaboration in a dynamic knowledge-sharing environment. Ontologies are consensual representations of a domain of discourse and the backbone of the future Semantic Web.
Research Guides: MUS 234: Harmony & Form IV: Find Books & Scores

A guide for students in Dr. Stanziano's MUS 234 class composing in a particular musical style.
Investigation And Analysis of Solar Energy Generation with Machine Learning Techniques

It is necessary for India to develop alternative energy sources in order to reduce its reliance on imported oil in order to attain energy stability. Furthermore, India's average per capita energy consumption is much lower than that of industrialised nations. The same is anticipated to increase substantially as a result of the fast economic growth and the possibility for fast industrialization that has occurred. Developing countries may benefit significantly from the use of sustainable renewable energy sources in the industrialization and development of their own countries. The overall demand for fossil fuels is expected to rise substantially in the near future as a consequence of continued industrial and economic expansion. Renewable Energy will not only assist India's energy supply, but they will also aid the nation in its battle against climate change. As a consequence of increased yields from carbon dioxide reduction permits under clean growth management, the feasibility of renewable energy sources is further enhanced. Overall, the clean energy industry, as well as the variety of uses for clean energy, has evolved and grown significantly in recent years. This paper examines the electricity generated by thermal generation, nuclear generation, and hydroelectric generation between 2017 and 2020. On the basis of secondary resources, the exploration is conducted. It is the primary goal of this study- data and display the data in a comprehensible manner. This study is also being used to the solar data in order to analyse the performance of the power system in time series and the loss over time. In this case, solar data from Kaggle was used in conjunction with a Python simulator. As a component of machine learning methods (ML-Algorithms), a neural network has been used to conduct data processing in time slices, and the loss and mean square error have been calculated in a certain time phase to evaluate solar energy and production as a power producing system. Overall, this research explores the photovoltaic cell, radiation patterns of solar energy generation.
Neural Spike Detection Circuit with Amplification Method using Input DC Level Control

A neural spike detection circuit is presented, in which all the amplifiers have an open loop architecture to reduce area and current consumption fundamentally. A new method for controlling the gain of an open loop amplifier is proposed. The gains of instrumentation amplifier (IA) and multiplier are controlled by a DC level of the input. In addition, an instrumentation amplifier (IA) includes a compensation circuit against the fabrication process corners to prevent amplification failure. The compensation circuit detects and adds threshold voltage variations in the compensation feedback loop. The circuit is fabricated using the standard 0.18 μm CMOS process. Changing the DC level at the IA input results in the amplification gain increase up to 650 V/V. The nonlinear amplification control capability of the multiplier is verified as the DC input level changes. The minimum detectable amplitude of neural spike is 100 μVpk. The overall power consumption is 1.4 μW. Integrated input-referred noise is measured to be 7.1 μVrms in a frequency range of 100 Hz to 10 kHz.
Representation of Generalized Human Cognitive Abilities in a Sophisticated Student Leaderboard

Gamification is a popular method for enhancing learners’ motivation and thereby strengthening learning efficiency. An example of gamification is the leaderboard, namely an approach showing a ranking of students. Although leaderboards are currently implemented in various domains, previous studies reported that they are solely based on one student characteristic, such as grade. This paper presents a sophisticated leaderboard showing a more reliable ranking of students. This leaderboard is available to both instructors and learners; instructors can be adequately informed by this ranking and redesign their teaching strategies, while learners can be motivated by the ranking and try more to advance their knowledge. The sophistication of this leaderboard lies in the employment of the Weighted Sum Model (WSM), which is the best-known multi-criteria decision analysis technique and responsible for evaluating a number of alternatives in terms of a number of decision criteria. The input of WSM is multiple learners’ characteristics, including current and previous knowledge, interaction time and frequency of misconceptions, so that a more robust representation of students is achieved. Our presented model was incorporated in an intelligent tutoring system for the computer programming language C#, and the evaluation results show high accuracy in the values of the leaderboard.
Blood Group Detection using Image Processing MATLAB

Blood grouping is one of the common and most essentiality for many of the major healthcare applications. Traditional way to determine the blood group involve human such as trained medical professionals which generally lead to human error. One of the solutions to overcome this issue is to automate and digitize this method. Image processing and computer vision techniques can be used for this purpose. Therefore, in this paper, we investigate the blood group detection using image processing techniques. For this purpose, experiment starts by taking images of sample blood slide as input and convert it into gray scale followed by binarization and canny edge detection. Finally, it decided the agglutination by counting detected edges. Performance of method is tested on real- time blood sample dataset. Experimental results show the accuracy of proposed method is comparable to real- time test.
A Study on the Actual Condition of Kindergarten Distance Education

This study attempted to provide implications for desirable distance education program by investigation the actual condition of kindergarten remote classes implemented to support the development and play of child in situations where face to face classes were not possible in the coronavirus era. 267 public and private kindergartens nationwide were randomly sampled by stratified sampling to investigate regional differences in distance education, differences in remote classes between national & public kindergartens and private kindergartens, and the composition of the contents of remote classes. Studies have shown that there are differences in the contents and composition of remote classes in large cities and small cities, public and private kindergartens. Most of the remote classes were prepared by the teacher in charge in the form of learning packages and delivered to parents in the form of drive - through so that children could learn at home. The study suggests that private kindergartens should also be able to use various remote class media provided by the Office of Education to resolve the unequal education and learning gap in the implementation of remote classes between public and private kindergartens. It also suggests that the educational gap between regions should be resolved by strengthening the capacity of teachers nationwide for remote classes. © 2021
APRIORI ALGORITHM FOR THE DETERMINATION OF THE GOODS SALES MARKET BANK

Data mining a process of finding meaningful new relationships, patterns, and trends by filtering the huge data stored in the database using pattern recognition techniques. One of the data mining techniques is the a priori algorithm. A priori algorithm is defined as an algorithm for finding the highest frequency patterns. Currently, the a priori algorithm has been implemented in various fields, one of which is in the field of business or trade and the field of education. Market basket analysis technique or market basket analysis is a data mining technique that aims to find products that are often purchased simultaneously from transaction data.Bina Karya Swalayan is a modern market that has various types of goods. Where in the supermarket there are still some problems faced by a manager and employees
Prova strutturata vs prova con svolgimento aperto: gli effetti sul rendimento degli studenti in Italiano

Didactic practices, i.e. choices and actions taken intentionally to realize an educational goal, play a crucial role in students' learning achievement. The aim of the present contribution is to examine the effects of teaching practices, in particular written composition tasks and structured tests, on the score obtained by students at the end of lower secondary education at the Italian tests of INVALSI (school year 2016/2017). To this end, we proposed hierarchical regression models, by controlling for some of the relevant variables at the student and class level. The datasets used are those of the INVALSI National Surveys and the Teacher Questionnaire of the school year 2016/2017.
Testing Dynamic SwiftUI Views

This chapter shows how to use Test-Driven Development to provide a dynamic data source to the ViewModel using Combine and how to leverage the tight integration between SwiftUI and Combine to make view updates seamless and reliable.
Neurally guided transfer learning for genetic programming

A key challenge in GP is how to learn from the past, so that the successful synthesis of simple programs can feed in to more challenging unsolved programs. In this work we present a transfer learning (TL) mechanism for GP which extracts fragments from programs it synthesises, then employs deep neural networks to identify new problems to deploy them into, to boost performance. This end-to-end system requires no human identification of which programs to use as donors for TL, the system only needs IO examples.
Оценивание речевого поведения блогеров читателями в русскоязычном сегменте сети Интернет (прагмалингвистический аспект)

This article presents the results of pragma linguistic experiments aimed at studying the way readers in the Russian-speaking segment of the Internet...