Reviewerly/exHarmony huggingface dataset:
Social intelligence and modern society

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
Introducing Programming Language Metrics

We introduce possibly the first approximation of programming language metrics that represent a spectrum over 70 unique and carefully gathered dimensions by which any two programming languages can be compared. Based on those metrics, one can evaluate her own `best' language, and to demonstrate how complex feelings such as “simplicity” and “easy to use”, often found as arguments in language debates and advertisements, can be decomposed into clear and measurable pieces. We put the collection as a completely separate open-source file (here as an appendix) so that everyone can participate in eliciting new and interesting dimensions used in programming languages research, development, and use. Metrics can find their use to compare languages, define requirements, create rankings, give tips for language designers, and simply provide a bird’s-eye view on existing languages features found in the wild.
Computer controlled image based object classification by using a robot manipulator

In this paper the design, implementation and application of a computer controlled vision system with a robot manipulator for classification of various fruits is introduced. The system, which consists of a RGB Camera, a PC and a five degree of freedom (DOF) robot manipulator, specifically set up to work in real-time applications. At the first stage of study the captured image of objects in RGB scale viewed by the camera is stored via USB link of computer as digital image data in registries for further segmentation process to recognize the objects. A particular Neural Network is designed to classify objects exactly. The moving objects on a belt, driven by a servo-stepper motor, are recognized in size and property via a vertically mounted camera. The recognized objects are then assorted by a computer controlled robot manipulator with five DOF and a gripper at the end-effecter, in order to transport the objects via a predefined trajectory to designated locations. As second stage, the kinematic link table of robot manipulator using inverse kinematics is determined, so that from the given way points of a particular trajectory the link parameters (joint angles) can be calculated. The joint angles are used as reference values for a joint based control algorithm programmed in C++. The joints are equipped with geared DC motors with position feedback. A particular control program is also used to control the motion of servo-stepper belt motor and the pneumatically activated gripper valve of the manipulator. Successful results are evaluated both in assorting of fruit in size, property, in synchronous working of the robot arm and in connection with image processing.
World’s first self-driving amphibious bus

Amphibious buses are extensively used worldwide for transporting people to and from tourist attractions across water and land. Although numerous studies on self-driving technologies have been reported, research on the automatic operation and navigation of an amphibious vehicle has been sparse; moreover, owing to the size of the amphibious vehicles, automatic transport of multiple people is not possible. Therefore, in this study, we attempted to realize unmanned operation of a sightseeing amphibious bus for 45 passengers. The bus was outfitted with a by-wire system. On the vessel side, an actuator, similar to that used in JOY cars, was installed to turn the captain’s steering wheel. We also developed a software for the automatic operation and navigation of the bus. The relationship between the car’s steering-wheel angle and the front-tire angles is linear, whereas that between the captain’s steering-wheel angle and the vessel’s rudder-plate angle is not (and was approximated with a sixth-order polynomial). Furthermore, Autoware—a leading autonomous-driving software utilizing model-based predictive control algorithms to control the steering wheel of automobiles—was employed in this work. These algorithms were altered using Nomoto’s KT vessel model equation to improve the accuracy of vessel-path tracking. To the best of our knowledge, till date, no studies have documented the functioning of self-driving vessels using predictive controls based on Nomoto’s KT vessel model equation. In accordance with the vessel navigation rules based on the Autoware obstacle avoidance logic, LiDAR, cameras, and sonars were employed to detect obstructions and give-way paths. Thus, we successfully demonstrated the working of world's first self-driving amphibious bus, with automated controls for entering/exiting water and during give-way operations.
Information technologies in the education system

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
Jaringan Syaraf Tiruan Mendeteksi Penyakit Pneumonia Infeksi Saluran Pernafasan Akut Dengan Algoritma Backpropagation

Acute Respiratory Infections (ARI) are a health problem that often affects children and adults. For adults, acute respiratory infections are mild or common, but in children under five, this disease is a threat that can cause death. One of the causes of death due to acute respiratory infections is incorrect diagnosis. This study aims to determine the level of accuracy and optimal neural network architecture in detecting ARI using the backpropagation method. This research was implemented using MATLAB software with several forms of network architecture. Symptoms of ARI that were used as input for detection of the disease consisted of 13 variables targeting non-pneumonia and pneumonia ARDs. Based on the research results, the architecture with the best configuration consists of 13 input layer neurons, 20 hidden layer neurons, and two output layer neurons with a binary sigmoid activation function (logs), a learning rate value of 0.5, an error tolerance value of 0.001, a maximum of the epoch of 216 and MSE 0.000997. Artificial neural networks with the backpropagation method used for weight adjustment can respond to training data and testing data well, marked by the resulting network accuracy of 100% in accordance with the desired target.&#x0D;
Efficient Deep Learning based Code Retrieval using Unified Graph Structure and Semantic Graph Matching Encoder

Abstract Computer programmers frequently use code retrieval systems to reuse existing code snippets from open-source repositories. These systems search a large collection of code snippets to retrieve the most relevant ones based on a text query. However, capturing semantic information between user queries and code snippets presents a key challenge in this task. Previous works have primarily used natural language processing (NLP) approaches to calculate relevance, but these approaches cannot capture deep structural and semantic dependencies between queries and snippets. In this study, we proposed a novel approach called Deep Graph-Based Semantic Representation and Matching (DeepGSR) for code retrieval. Our approach transforms queries and code snippets into a unified graph-structure representation and then retrieves relevant code snippets using a novel graph matching and retrieval approach. DeepGSR learns fine-grained relevance between queries and code snippets using cross-attention-based semantic matching operators, in addition to extracting structural information. We evaluated our approach using two publicly available code retrieval datasets, and the experiments demonstrate that DeepGSR efficiently retrieves relevant code snippets for a given query and significantly outperforms existing code retrieval approaches.
Analisis Topic Modelling Persepsi Pengguna Internet Menggunakan Metode Latent Dirichlet Allocation

Technological advances have undoubtedly had a major impact on information media. One impact of technological progress is the existence of news media as a source of publik information. There is also regional information, both domestic and foreign, and of course there are various discussions. News data from online news portals can be used as a source of information as a source of research and analysis. Of course, newa portals cover all types of news on various topics. Indentifying frequently discussed topics on news portal will denfinitely take a lot of time. Therefore, this research focuses on applying a topic modelling system to implement a news topic decision system using the Latent Dirichlet Allocation (LDA) method. This research successfully applies the Latent Dirichlet Allocation (LDA) method in determining news topics, of which there are three topic categories that are often discussed on the online news portal detik.com. topic 1 contains natural disaster event, topik 2 contins political figures and issues, topik 3 conttains news about the world cup.
A Study on the ‘describe regret in the poem with a hundred rhymes(百韻詩)’ by Lee Min-gu(李敏求), Dongju(東州)

This paper aims to examine the background and characteristics of the poem of ‘describe regret in the poem with a hundred rhymes’ by Lee Min-gu, Dongju, and to give its significance of the literary history. This poem was written by Dongju in the 17th century in Yongbyon, a place of exile. In particular, This poem tells us that Dongju was influenced by Doo-bo(杜甫) poetry during the exile period. And in the middle of the Joseon Dynasty, many works that borrowed a poem of Doo-bo’s the poem with a hundred rhymes were gradually produced and developed. This poem draws attention in literary history because it was built during such a period.&#x0D; As a result, the following could be found as characteristics of the poem with a hundred rhymes by Dongju. At the beginning of the poem, Dongju started magnificently by focusing on his family and life with the prosperity of the Joseon Dynasty. Subsequently, he recalled the Manchu war of 1636, described the battlefield, and expressed his feelings in exile through quotes from the Chinese literature of 辭賦. However, Dong-ju showed the process of mentally recovering through his daily life. The poem with a hundred rhymes of Dongju is different from the poem with a hundred rhymes of the mid-Joseon Dynasty. After that, it influenced the construction of Baegunmansi(百韻輓詩) by Namin(南人) writers in the late Joseon Dynasty. And as a poem built in exile, it was able to reveal its significance in that it was differentiated while continuing its genealogy.
Selective Pressure Strategy in Multilevel Cooperative Coevolution for Solving LSGO Problems

Continuous large-scale global optimization (LSGO) is a challenging task for a wide range of state-of-the art metaheuristic algorithms. This is due to the curse of dimensionality because the size of the search space grows exponentially with the increasing the number of variables. Thus, metaheuristics lose efﬁciency in solving LSGO problems. For increasing the performance of metaheuristic algorithms in solving LSGO problems, cooperative coevolution (CC) is used. CC-based metaheuristics have two main control parameters, which are the population size and the type of variable grouping. In this paper, a novel self-adaptive multilevel cooperative coevolution algorithm is proposed. The subcomponent optimizer of the proposed CC-based algorithm is SHADE. The proposed algorithm self-adapts the number of subcomponents and the population size during the optimization process. The complete title of the proposed algorithm is CC-SHADE-ML. We have evaluated the performance of the proposed algorithm on fifteen benchmark problems chosen from the LSGO CEC’2013 benchmark set. The performance of the CC-SHADE-ML algorithm has been evaluated using well-known mutation strategies and selection operators. We can conclude that mutation and selection operators make a significant impact in the performance of DE-based metaheuristics. All numerical experiments are proven statistically.
Adaptive Differential Evolution With Two Populations of New and Best Individuals

In this paper a novel algorithmic scheme for differential evolution is proposed with two populations and new mutation strategies. The populations contain latest and best individuals, and the sizes of these populations are controlled independently. The LL-NADE algorithm uses the parameter adaptation scheme proposed in L-SHADE, and the sizes of both populations are reduced at the same time. The experiments described in the paper are performed on the CEC 2022 benchmark suite. The total standard score is used for comparison of different algorithms on a set of benchmark problems. The experiments are performed with six different mutation strategies, which utilize individuals from both populations. The most efficient mutation strategy utilizes directed search from newest to top individuals. It is shown that LL-NADE is capable of demonstrating results comparable with state-of-the-art algorithms. Comparison on various test functions and dimensions have shown that LL-NADE performs in a way that is not similar to other differential evolution algorithms, achieving better results on some problems, but worse on other.
An Efficient Training Algorithm of Restricted Boltzmann Machines

The paper deals with an actual applied problem related to the artificial neural networks training. An approach to the solution based on the idea of random search is proposed. An original training algorithm that implements Boltzmann annealing has been developed and its convergence in probability to the global optimum has been proved. It is also shown that the proposed algorithm can be easily modified to train any artificial neural network. Thus, it has a good prospect for solving applied problems using neural network technologies in general. Experimental studies have been carried out, in which, using the example of compressing color raster images problem, the proposed algorithm was compared with the known adaptive moment algorithm - one of the best gradient methods for training neural networks. Image compression was performed using an ensemble of n Gauss-Bernoulli restricted Boltzmann machines. The use of an ensemble of n machines in combination with a specially developed parallelization procedure made it possible to reduce the computational complexity of the training process and increase the speed of the proposed algorithm. As a result of experiments, it was shown that the proposed approach is not inferior to gradient methods in terms of speed. Moreover, the developed training algorithm turned out to be more than twice as effective as the adaptive moment algorithm in terms of the quality of the solution obtained.
Disk Drives Remaining Useful Life Prediction Using the Extreme Learning Machine

The article deals with the problem of predicting the remaining useful life of disk drives using a machine learning model, in particular, using an Extreme Learning Machine (ELM). A method is proposed for improving the values of model quality metrics by generating new features, as well as their selection using a method that implements the calculation of the symmetric Kullback-Leibler Divergence (SKLD). It is shown that a model based on an extreme learning machine and trained on the basis of a dataset formed from the results of generation of new features and their subsequent selection by the SKL method can predict the remaining useful life with an average error of 2.5 days, while model training time is about 6 seconds. The results of a comparative analysis are presented, confirming the efficiency of the proposed model based on ELM. Additionally, the methods for generating features BY and MI are compared, and their shortcomings over SKLD in this case are demonstrated.
Hybridization of Machine Learning Models and Differential Evolution in Data Mining

Nowadays, data mining methods are applied in various fields of science and technologies. The widespread application of these methods necessitates the development of procedures to improve the efficiency of basic methods. Often, the efficiency of approaches based on data mining methods significantly depends on the selection of numerical coefficients. Moreover, these numerical coefficients can be both parameters of the algorithm and parameters of the obtained solutions. Therefore, it is possible to apply optimization methods to determine the best values. Nowadays, one of the most efficient methods of real optimization is differential evolution. The paper presents the study the differential evolution application to optimize coefficients of a dynamic system model obtained by an approach based on a self-configuring genetic programming algorithm. Also, it considers optimization of threshold values for solving classification problems with decision trees. The paper considers the most popular methods for training decision trees. The selected tasks demonstrate the universality of the proposed modification, since they solve diverse tasks. Moreover, the applied data analysis methods belong to different classes. The results of numerical experiments are presented for these tasks. They prove efficiency of the proposed hybridization.
Case Report: Perceptions of the Bajo Community on Children's Education in Bajo Indah Village, Soropia District, Konawe District

This study aims to determine public' perceptions of their children's education in Bajo Indah Village, Soropia District, Konawe Regency. The instruments used in this research for collecting data are interviews and questionnaires. Data analysis techniques used in this study used qualitative approaches. The percentage of Bajo people's perceptions of their children having to take the education path shows 90% stating positively that their children are to take the education path and 10% of subjects showing no response to the statement that children must take education. The percentage of subjects believing that their children have the motivation and responsible attitude created by the educational process is 71.4%. Their perception of school facilities and infrastructure in the Bajo Indah village area showed that 62% of the sample responded positively and considered the facilities and infrastructure sufficient, while the other 38% thought they were inadequate. Percentage results for the Bajo community's perceptions of the Bajo Indah Village Environment having a positive effect on children's education showed that 57% of the sample stated positively. In comparison, 43% thought the Bajo Indah Village Environment had an unfavorable effect on children's education. From all the percentage analyses above, the researcher took the research results on the importance of children's education in the Bajo Indah village, resulting in 86% positive and 14% negative perceptions that developed in the perceptions and mindset of the Bajo people.&#x0D; &#x0D; &#x0D; Received: 3 January 2023 / Accepted: 28 February 2023 / Published: 5 March 2023
State Regulation of Otkhodnichestvo in the 1920s (On the Materials of Siberia)

The article, based on the materials of the Siberian region, examines the phenomenon of otkhodnichestvo that existed in pre-revolutionary Russia and revived under the Bolshevik rule in the early 1920s.This process was generated by the desire of the peasants to earn extra money outside the places of permanent residence, was temporary, most often seasonal, after which the population returned home for agricultural work.During the period of the empire, such a process most often had an unorganized character, although the government tried to control seasonal industries, but it did not succeed in doing this in full.The Soviet leadership, starting from the second half of 1921, is gradually trying to resolve this problem by establishing state control over unregulated waste.The study clarifies the methods of control over otkhodniks, the results of the attempts made, as well as the scale of otkhodnichestvo in the whole country and in Siberia.
The Role of Digital Resources in Enhancing the Quality of Faculty Members’ Scientific Life at Princess Nourah Bint Abdulrahman University

The purpose of the study was to identify the impact of digital resources in enhancing the quality of faculty members' scientific life at Princess Nourah Bint Abdulrahman University. It also aimed to investigate the role of digital resources in achieving job satisfaction for faculty members, including academic promotion and the quality of scientific research at global publishing databases. The study used the analytical survey method. The questionnaire was also utilized as the primary tool for data collection and to measure the impact of digital resources in achieving the quality of faculty members' scientific life. The study sample consisted of (114) Saudi female faculty members representing all scientific disciplines at the university. The study reached significant results indicating that the university's digital resources are one of the most important resources for enhancing the quality of faculty members' scientific and academic life. Therefore, the study recommended that faculty members should participate in setting policies and decisions related to their field of work. The study also recommended entrenching the value of transferring, using, and managing digital knowledge comprehensively. Besides, supporting in-depth studies that achieve the quality of the academic life cycle at Princess Nourah Bint Abdulrahman University.&#x0D; &#x0D; Received: 22 January 2023 / Accepted: 25 February 2023 / Published: 5 March 2023
Credit Card Attrition Classification Through Neuronets

Credit card customers comprise a volatile subset of a banks' client base. As such, banks would like to predict in advance which of those clients are likely to attrite, so as to approach them with proactive marketing campaigns. Neuronets have found great application in many classification problems. Credit card attrition is a poorly investigated subtopic that poses many challenges, such as highly imbalanced datasets. The goal of this research is to construct a feed-forward neuronet that can overcome such obstacles and thus accurately classify credit card attrition. To this end, we employ a weights and structure determination (WASD) algorithm that facilitates the development of a competitive and all around robust classifier whilst accounting for the shortcomings of traditional back propagation neuronets. This is supported by the fact that when compared with some of the best performing classification models that MATLAB's classification learner app offers, the power softplus activated WASD neuronet demonstrated either superior or highly competitive performance across all metrics.