Reviewerly/exHarmony huggingface dataset:
HOW DOES LOCAL CONTENT TEXT BASED SHARING READING LITERACY MODEL AFFECT THE INDONESIAN LANGUAGE COURSE IN INDONESIAN SENIOR HIGH SCHOOL? (BAGAIMANA MODEL SHARING READING LITERACY BERBASIS KONTEN LOKAL BERPENGARUH PADA PELAJARAN BAHASA INDONESIA DI SEKOLAH MENENGAH ATAS?)

This study aims to determine the effect of reading material of Indonesian course by student and teacher in Senior High School. The type of reading is in the form of folklore in the West Sumatra area. This research used quantitativeapproach towards descriptive method. It is to describe the learning effect for students and teachers, where it is related to master a reading with critical reading learning using the Sharing Reading Literacy model. The research subjects were students of class X SMAN 15 Padang studying critical reading material and consisting of 10 classes. The instruments used were questionnaire sheets and interview guidelines. Data were analyzed by using descriptive statistics. Based on the results of data analysis, it shows that 74% of students need critical reading learning resources, 73% of student need critical reading learning media, and 58% of student need critical reading learning methods called Sharing Reading Literacy as required by the learning indicators. It shows that the local content text based sharing model affect the Indonesian Language course in Indonesian Senior High School.
KOLMOGOROV-WIENER FILTER FOR CONTINUOUS TRAFFIC PREDICTION IN THE GFSD MODEL

Context. We investigate the Kolmogorov-Wiener filter weight function for the prediction of continuous stationary telecommunication traffic in the GFSD (Gaussian fractional sum-difference) model.&#x0D; Objective. The aim of the work is to obtain an approximate solution for the corresponding weight function and to illustrate the convergence of the truncated polynomial expansion method used in this paper.&#x0D; Method. The truncated polynomial expansion method is used for the obtaining of an approximate solution for the KolmogorovWiener weight function under consideration. In this paper we used the corresponding method on the basis of the Chebyshev polynomials of the first kind orthogonal on the time interval on which the filter input data are given. It is expected that the results based on other polynomial sets will be similar to the results obtained in this paper.&#x0D; Results. The weight function is investigated in the approximations up to the eighteen-polynomial one. It is shown that approximations of rather large numbers of polynomials lead to a good coincidence of the left-hand side and the right-hand side of the Wiener-Hopf integral equation. The quality of the coincidence is illustrated by the calculation of the corresponding MAPE errors.&#x0D; Conclusions. The paper is devoted to the theoretical construction of the Kolmogorov-Wiener filter for the prediction of continuous stationary telecommunication traffic in the GFSD model. The traffic correlation function in the framework of the GFSD model is a positively defined one, which guarantees the convergence of the truncated polynomial expansion method. The corresponding weight function is obtained in the approximations up to the eighteen-polynomial one. The convergence of the method is illustrated by the calculation of the MAPE errors of misalignment of the left-hand side and the right-hand side of the Wiener-Hopf integral equation under consideration. The results of the paper may be applied to practical traffic prediction in telecommunication systems with data packet transfer.
SOCIAL RESPONSIBILITY OF A SCIENTIFIC INSTITUTION: SOCIO-MANAGERIAL ANALYSIS

Objective of the study is to conduct a comprehensive socio-managerial analysis of the state and problems in the field of social responsibility of scientific institutions of the Russian Federation and developing scientific and practical recommendations on the implementation of the social responsibility management model in the industry based on its results. &nbsp; The methodological basis of the. The publication is based on the application of system-structural, concrete historical, dialectical methods in the study of objective social phenomena. To clarify aspects related to the practice of managing the social responsibility of scientific institutions in Russia, a survey was conducted of experts - heads of departments / areas, social management of five scientific institutions leading in subject areas of research in five different regions of the country, including one of the largest universal research universities at the federal level. &nbsp; Research results. The notion that the institution of social responsibility contributes to the integral development of scientific institutions as social organizations through improving the quality of management and resistance to negative environmental fluctuations, meanwhile, due to a number of objective reasons (such as lack of competencies and knowledge) and subjective (such as the lack of will of the founders and management to implement social innovations) the nature of the introduction of social responsibility mechanisms in the Russian scientific sphere is stalling and still remains at an embryonic level. Practical recommendations have been developed for the implementation of the social responsibility management model for scientific institutions. &nbsp; Prospects of the study. Conducting detailed in-depth assessments of the current state and practice of introducing social responsibility in a large sample of scientific institutions with differentiation by form of ownership, region of presence, size (including headcount), priority areas of scientific research.
ABOUT THE THIRD RUSSIAN PHILOSOPHICAL CONGRESS: TWENTY YEARS LATER

The article reconstructs the main events and ideas of the III Russian Philosophical Congress held in Rostov-on-Don in 2002. Quantitative characteristics of the events, topics of plenary and breakout sessions, colloquiums and round tables are given. The chronology of events and the emotional and intellectual atmosphere of the congress are being restored. The article is not limited to memorial tasks only. A number of theoretical positions containing an analysis of the socio-cultural situation at the turn of the second and third millennia, opening the space of modern social consciousness, are given. The realities of this world – reason, science, morality, their convergence and divergence and their civilizational differences are the subject of ongoing discussions. The discussion of the future of Russian philosophy, held at the congress, and its fate is characterized. Attention is focused on the problem of life meanings that are significant for society and central to philosophy. The role and significance of the ideas of the Russian Philosophical Congress held in Rostov-on-Don for modern intellectual life is substantiated.
FORMATION OF THE CREATIVE PERSONALITY OF THE YOUNGER GENERATION IN THE MODERN ARTISTIC AND EDUCATIONAL ENVIRONMENT OF THE UNIVERSITY

The purpose of the study is to study the phenomenon of art in the formation of a creatively developed personality of the younger generation in the system of art education of higher education in the conditions of an information digital society. The methodological basis of the. The process of transformation of the educational space in the conditions of an information digital society involves the use of a systemic transdisciplinary approach based on the presence of a cultural code as an invariant in all disciplines and aspects studied, value-orientation, personal, communicative-activity, dialectical approaches. The research is based on the concepts of humanistic, personality-oriented education, individual development of students for the formation of a developing, creative personality of students in an artistic environment. &nbsp; Research results. In general, the society and specialists of the higher school were given the state goal of becoming a personality of the younger generation − an educated, creative personality, oriented in information flows, with a firm civic position, brought up on cultural traditions[1]. In the context of the formation of the information society and the developing system of digital education, the problem of the formation of creative abilities of students in the conditions of humanistic development of this process arises. The digital environment contains risks and problems. At the same time, the use of new technologies creates additional prospects: informational, technological, related to saving time on the creative process. Which also contributes to the formation of a student's creative personality in new conditions. The digital environment participating in the educational space contains a certain base and capabilities that are already being successfully used at the university to optimize learning within the framework of forms of education: full-time, distance and additional. The system of training sessions should be organized taking into account the use of digital technologies in their maximum application for artistic specialties. Prospects of the study. The content of the scientific and methodological base and the variety of visual information provides a full-fledged learning process and creative realization of the personal potential of the student of the art direction and designer. In our opinion, the use of project activities in several variants is of great importance in the creative development of a student: author's, group and accompanied by university partners. This creates additional motivation for learning the cultural values of society, familiarity with new opportunities in creativity and a new creative environment. &nbsp; &nbsp; [1] National project "Education" / State administration – URL: https://strategy24.ru/rf/education/pro jects/natsionalnyy-proekt-obrazovanie (date of application: 02.08.2022).
HYBRID MULTI-CRITERIA DECISION MAKING MODEL FOR CUSTOMER KNOWLEDGE MANAGEMENT IN SOFTWARE INDUSTRY

In this study, the enablers of Customer Knowledge Management (CKM) software companies are investigated. Consequently, the conceptual CKM enabler model is developed by extracting “organizational”, “technological” and “human” factors from the literature. Then, a Multi-Criteria Decision-Making (MCDM) approach is applied to identify the dependence relationships of factors by using the Analytic Network Processes (ANP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. The inter-dependencies significance among the factors is evaluated by DEMATEL. ANP technique is used in order to clarify the relative significance of the factors and identify the weight and priority. The results show that the “organization” dimension is the most considerable aspect of enhancing CKM in software development. The “organization” aspect has more interaction with other aspects and activates the “technology” and “human” factors. In addition, “technology” is the second dimension that influences “human” and “organization”. Due to the importance of CKM for organizations, this research provides a significant contribution in the context of CKM by extracting and ranking the CKM antecedent factors for enterprise software development context.
Self scale estimation of the tracking window merged with adaptive particle filter tracker

Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.
Characterization of cadmium sulfide light dependent resistors sensors for optical solar trackers

&lt;span lang="EN-US"&gt;The aim of this paper is to study the effect of dissimilarity of the intrinsic characteristics of the light dependent resistor (LDRs) on optical sun tracking systems, designed for solar power concentration applications such as parabolic trough collectors, Fresnel mirrors concentrators, and concentrated photovoltaic, a comparative study was done between a sun tracker based on LDRs chosen randomly with and without an initial calibration of the offsets, and a sun tracker based on LDRs selected meticulously thanks to a black box test bench, developed especially for this purpose. By choosing two light dependent resistors randomly, the dissimilarity between them can reach 23.2%, which cause a bad sun tracking even with initial offset calibration, in the other hand, and by the use of selected LDRs using the test bench, the dissimilarity drops to 0.06%, which meets requirements of solar power concentration systems.&lt;/span&gt;
AgroSupportAnalytics: big data recommender system for agricultural farmer complaints in Egypt

&lt;span&gt;The world’s agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers’ agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and ﬁltering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%.&lt;/span&gt;
Propose shot boundary detection methods by using visual hybrid features

&lt;span lang="EN-US"&gt;Shot boundary detection is the fundamental technique that plays an important role in a variety of video processing tasks such as summarization, retrieval, object tracking, and so on. This technique involves segmenting a video sequence into shots, each of which is a sequence of interrelated temporal frames. This paper introduces two methods, where the first is for detecting the cut shot boundary via employing visual hybrid features, while the second method is to compare between them. This enhances the effectiveness of the performance of detecting the shot by selecting the strongest features. The first method was performed by utilizing hybrid features, which included statistics histogram of hue-saturation-value color space and grey level co-occurrence matrix. The second method was performed by utilizing hybrid features that include discrete wavelet transform and grey level co-occurrence matrix. The frame size decreased. This process had the advantage of reducing the computation time. Also used local adaptive thresholds, which enhanced the method’s performance. The tested videos were obtained from the BBC archive, which included BBC Learning English and BBC News. Experimental results have indicated that the second method has achieved (97.618%) accuracy performance, which was higher than the first and other methods using evaluation metrics.&lt;/span&gt;
Flexible Least Squares Algorithm for Switching Models

The self-organizing model and expectation-maximization method are two traditional identification methods for switching models. They interactively update the parameters and model identities based on offline algorithms. In this paper, we propose a flexible recursive least squares algorithm which constructs the cost function based on two kinds of errors: the neighboring two-parameter estimation errors and the output estimation errors. Such an algorithm has several advantages over the two traditional identification algorithms: it (1) can estimate the parameters of all the sub-models without prior knowledge of the model identities; (2) has less computational efforts; and (3) can update the parameters with newly arrived data. The convergence properties and simulation examples are provided to illustrate the efficiency of the algorithm.
Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure

&lt;span lang="EN-US"&gt;Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, &lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;k&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains.&lt;/span&gt;
Bit-based cube rotation for text encryption

&lt;span&gt;Today's rapid technological developments make information increasingly important. Not just its content, but the channels or media used for information distribution also need to be secured. Information security is an important aspect that requires serious attention. One of the most important parts of information security is implementation of encryption using certain methods or techniques. This study proposes bit-based cube rotation to secure a plaintext. The aim is to produce a ciphertext that satisfies the two properties of cryptography through diffusion to produce confusion. The result shows that in a normal sentence, there is a significant change in the ciphertext which has the highest avalanche effect value of 55.47% and a correlation coefficient of 0.115. This result proves that the bit-based cube rotation can produce a good ciphertext, where the encryption result is not influenced by its original text.&lt;/span&gt;
A new method for nonlinear state estimation problem

In this paper, a new filtering technique to solve a nonlinear state estimation problem has been developed with the help of the Gaussian integral. It is well known that for a nonlinear system, the prior and the posterior probability density functions (pdfs) are non-Gaussian in nature. However, in this work, they are assumed to be Gaussian; subsequently, the mean and the covariance are calculated. In the proposed method, nonlinear functions of process dynamics and measurements are expressed in a polynomial form with the help of the Taylor series expansion. In order to calculate the prior and the posterior mean and covariance, the functions are integrated over the Gaussian pdf with the Gaussian integral. The performance of the proposed method is tested on three nonlinear state estimation problems. The simulation results show that the proposed filter provides more accurate results than other existing deterministic sample point filters such as the cubature Kalman filter, the unscented Kalman filter, and the Gauss-Hermite filter.
PERANCANGAN DAN PENERAPAN SISTEM DETEKSI DAN PELAPORAN KECELAKAAN BERBASIS SMS

As transportation technology develops, the vehicles used for these activities are getting faster. Driving at high speed can increase the risk of accidents and the risk of traffic accidents for the driver. As an anticipatory step from the impact of a more severe accident, the authors design and implement an accident detection system that will provide SMS in the form of vehicle location data and vehicle details to the family of the accident victim when the system detects an accident The system built uses Arduino UNO components as the controller, accelerometer sensor MPU6050 as accident detection sensor, NEO6MV2 GPS Module as vehicle location module and SIM800L Module as SMS notification sending module. The system was built using Arduino IDE as an application program. The Accelerometer Sensor has a good system stability with a standard deviation of 0.024. The GPS sensor has a lock time that is quite fast and less than 3 minutes with good lock time stability. The GSM SIM800L module has a fairly good delivery delay with the largest delay of 3.95 seconds and the fastest delivery delay of 2.16 seconds. For further development of the tools that have been made, it is necessary to use better hardware so that the system's work performance can be better. It has a shorter GPS lock time and a shorter delay in sending SMS notifications.
CLOUD DATA AUDITING BASED ON ATTRIBUTE WITH HASHING TECHNIQUE

Cloud storage and outsourced storage will assist to manage the load of data owners.Cloud storage is risky despite its perks.Data authenticity is a key component of outsourced cloud services.A verifier may examine Cloud-server files without downloading the whole file, decreasing communication overhead.Existing systems need public keys or a precise ID, which is inflexible.In this paper, customers may submit files with certain qualities and pick an auditor to ensure the accuracy of outsourced material.By proposing attribute, we intend to address a Key Management problem with cloud data morality.This paper provides a "Attribute-Based with Hashing" approach for auditing cloud data.This collusion-resistant method protects attribute privacy as it uses hashing technique.Diffie Hellman and discrete algorithm assumptions provide our protocol's coherence.Finally, a protocol prototype is built on this protocol.