Reviewerly/exHarmony huggingface dataset:
A New Fuzzy Inference System Applied to Time Series Forecasting

Fuzzy systems are a class of machine learning introduced by Zadeh that combine accuracy and interpretability. This class of models consists of two main parts, the antecedent and the consequent. While the antecedent is responsible for modeling the inputs, the consequent concerns modeling the output. The literature reports two main types of fuzzy systems: Mamadani and Takagi-Sugeno. While Mamdani uses fuzzy sets in the consequent part, Takagi-Sugeno uses polynomial functions. Consequently, Mamdani provides better understandable models and Takagi-Sugeno more accurate ones. In this paper, we propose a new Takagi-Sugeno model. Still, instead of defining the rules based on the input, the proposed model designs the rules based on the output variation to capture linearities in the output and clusters them in the same rule. The model is applied in the regression problems of benchmark series and real datasets of power transformers. The performance of the proposed model is compared with the performance of classical models and evolving Fuzzy Systems. The results are evaluated using error metrics, the number of final rules, and runtime.
Development and validation of a self-assessment-based instrument to measure elementary school students’ attitudes in online learning

Online learning during the COVID-19 pandemic makes it difficult for teachers to assess student learning attitudes. Limited availability of instruments to measure attitudes of students when they are engaged in online learning leads to difficulty of teachers to conduct appropriate assessments on that measure. The current study, therefore, mainly was intended to produce a self-assessment-based instrument that is feasible to use to measure students’ attitudes in online learning. In order to produce such instrument, we used developmental research method by following steps in the instrument design that is proposed by McCoach, Gable, and Madura. Furthermore, in order to provide feasibility of our instrument, we provided evidence of content validity through experts’ judgment data as well as evidence of construct validity with confirmatory factor analysis (CFA) and reliability estimation with Cronbach’s α through a limited trial and an expanded trial using response data of sixth graders of elementary school engaged in online learning. Our study has produced a self-assessment-based instrument that uses a summated rating scale and is composed of six components (i.e., honest, disciplined, responsible, polite, caring, and self-confident) and 24 items that have demonstrated evidence of content validity, stable factor structure, and high reliability estimates.
Smartphone application for assessing teacher performance

This work seeks to design a smartphone application for assessing teacher performance utilizing a 4D development model modified with the Mardapi instrument development model of (1) define (defining); (2) design (deciding instrument requirements, determi­ning application media specifications, creating flowcharts, and creating storyboards); (3) develop (assemble instruments, code, execute tests, evaluate test results, and interpret measurement findings); and (4) disseminate (spread). Proof of the application’s viability was established through product testing with 26 principals acting as application users and evaluations by material and media specialists. The content validity of the acquired results was evaluated using the Aiken method, and the construct validity was evaluated using PLS (Partial Least Squares) analysis because the sample size does not have to be large, in accordance with this study where the number of samples is not so large. Using Cronbach’s alpha to evaluate the reliability. The results demonstrated that the built application was “possible,” as proven by the existing instruments in the valid and trustworthy application. This is proven by the fact that the content validity test utilizing the Aiken method yielded good validity results, with a mean score of 0.80 for material experts and 0.75 for media experts. The construct validity test using PLS (Partial Least Squares) yielded positive results for validity. The estimation of reliability using Cronbach’s alpha yields positive findings. Therefore, it can be concluded that the developed application for teacher performance evaluation possesses the features of a valid and trustworthy assessment instrument.
Mitigating Data Bias in Healthcare AI: Strategies and Impact on Patient Outcomes

Data bias in healthcare artificial intelligence (AI) models poses a significant challenge to equitable and accurate patient care. This study explores strategies for mitigating data bias in healthcare AI systems and investigates the resulting impact on patient outcomes. Through a comprehensive analysis of existing bias detection and reduction techniques, as well as fairness-enhancing algorithms, this research aims to shed light on effective approaches for creating more balanced and unbiased AI models. By examining real-world case studies and evaluating the implications of bias reduction, this study provides insights into how addressing data bias can lead to improved patient care, diagnosis accuracy, and treatment recommendations. The findings underscore the critical role that bias mitigation plays in promoting fairness, ethics, and quality in healthcare AI applications, emphasizing the importance of on-going efforts to enhance the accuracy and reliability of AI-driven healthcare solutions.
Deep Image Compression Perceptual Reconstruction Network Based on Channel Attention

This paper proposes a depth image compressed sensing image reconstruction based on channel attention to solve the problem that the image reconstruction algorithm based on deep learning cannot well recover the original signal from the measured value when the sparsity distribution of the signal is uneven. construct a network. The proximal gradient descent algorithm (PGD) and the deep unrolling network (DUN) are combined to build a compressed sensing image reconstruction model. A channel attention mechanism is added to the encoder and decoder of the proximal mapping module (PMM) to help the model focus. Focus on important information, improve the accuracy of signal recovery, and better restore the original signal. At the same time, Charbonnier loss and edge loss have been improved to better adapt to different tasks and sampling rate requirements. Experiments show that this model algorithm can effectively improve the reconstruction accuracy of image compressed sensing.
Spark Checkpoint Fault Tolerance Strategy Based on Hybrid Storage

Spark, a parallel computing framework, performs data processing in-memory to minimize disk access and enhance data processing efficiency. However, Spark does not take into account intermediate data results of jobs. In the event of a cluster node failure, the loss of processing data blocks occurs. Recomputing based on lineage incurs a high cost. To address the challenge of low data recovery efficiency resulting from Spark's lineage-based fault tolerance, this paper introduces the Resilient Check-pointing Strategy based on Hybrid Storage (RCSA). The paper first analyzes the Spark checkpointing process, followed by presenting the design of the hybrid storage module. Finally, it proposes a checkpoint selection algorithm based on multi-branch points. Experimental results indicate that this strategy effectively reduces Spark's backtracking overhead and improves job recovery efficiency.
Leveraging AI for Enhanced Software Effort Estimation: A Comprehensive Study and Framework Proposal

This paper presents an extensive study on the application of AI techniques for software effort estimation in the past five years from 2017 to 2023. By overcoming the limitations of traditional methods, the study aims to improve accuracy and reliability. Through performance evaluation and comparison with diverse Machine Learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVM), Linear Regression, Random Forest and other techniques, the most effective method is identified. The proposed AI-based framework holds the potential to enhance project planning and resource allocation, contributing to the research area of software project effort estimation.
The Future Value of the Metaverse for Funeral and Interment

With the global aging population and the increasing occurrence of natural disasters, the number of deaths worldwide is on the rise. Different cultures have developed various burial practices to honor and remember the deceased. However, as we advance in science and technology and face the depletion of natural resources, it becomes crucial to address the need for effective funeral and interment reform. One potential solution lies in funeral and interment services based on the metaverse. By leveraging different technologies, the metaverse can provide innovative solutions to fulfill various funeral and interment requirements. This approach offers a new and unique experience for funeral and interment and interment ceremonies, catering to the diverse and personalized funeral and interment needs of people in the modern era. Furthermore, it can contribute to the ongoing process of funeral and interment reform, supporting the development of a contemporary funeral and interment ritual system that aligns with the needs of a civilized society.
Sequential Series-Based Prediction Model in Adaptive Cloud Resource Allocation for Data Processing and Security

A developed adaptive forecasting model for cloud resource allocation is presented. It employs principal component analysis on a sequence of virtual machine (VM) requests. Requests are processed to detect anomalies, and adaptive predictions are computed using EEMD-ARIMA or EEMD-RT-ARIMA methods. The choice between EEMD-ARIMA and EEMD-RT-ARIMA methods is determined by comparing the execution time values ${\mathrm {R}}_{\mathrm {{i}}}$ (sequential series test) with the threshold value ${\mathrm {R}}_{\mathrm {{t d}}}$. If ${\mathrm {R}}_{\mathrm {{i}}} \gt {\mathrm {Rtd}}$, EEMD-ARIMA is used; if ${\mathrm {R}}_{\mathrm {{i}}} \leq {\mathrm {R}}_{\mathrm {{t d}}}$, EEMD-RT-ARIMA is applied. This adaptive approach enables the selection of a prediction method based on data characteristics and resource demands. To optimize the selection of the ${\mathrm {R}}_{\mathrm {{t d}}}$ threshold, the impact on accuracy and time costs is examined. A quartile method is utilized to detect dynamic spikes, and cubic spline interpolation is employed to smooth data. EEMDRT-ARIMA-based forecasting enhances accuracy through preprocessing of dynamic spikes and adaptive method selection. Calculations of time costs indicate that this method reduces forecasting time by 1.5 times by extracting core component sequences.
Penerapan Naïve Bayes Pada Penentuan Lokasi Waralaba

Franchising is currently one of the solutions in developing the economy, various sectors already have types of franchises including food and retail. In this study, the researcher applied the naïve Bayes algorithm in determining franchise locations according to several attributes including road conditions, amount of investment, parking area, existing barriers, visibility, traffic patterns, workforce, age, workforce, types of consumers. The probability obtained from the results of the study shows that food franchises are the best result with a probability value of 0.605 and franchises with a high prospect label are at Alfamart with a probability value of 0.368. In the franchise dataset research with the naïve Bayes algorithm, an accuracy value of 88.10% is obtained, thus Naïve Bayes can accurately predict the most profitable franchises
Impact of mBlock Interface Design on Student Interest and Motivation in Primary School Robotics A Case Study in Indonesia

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
Digital poster as an object of the latest media spaces

We discuss new hybrid forms of digital posters that are developing in the digital environment. Special attention is given to interactive forms using three-dimensional space and augmented reality technologies as new means of artistic expression and communication.
Problems Faced and Coping Strategies during Online Teaching by EFL Teachers at Secondary Level

This research explored the challenges faced by EFL (English as a Foreign Language) teachers in the realm of online teaching/learning and the strategies to overcome these obstacles. The objectives were to explore the challenges of EFL teachers during online teaching classes and to suggest coping strategies for EFL teachers. Twenty teachers were selected randomly from five secondary schools. The study employs a qualitative approach, via in-depth interviews, to explore the various hurdles EFL learners encounter in online learning. Findings revealed many challenges including limited face-to-face interaction, technological issues, self-motivation, and the absence of immediate feedback. The study also illuminates the coping strategies employed by EFL learners, such as leveraging online resources, forming virtual study groups, and adopting time management techniques. The thematic analysis produced five conclusions. Teaching materials, corrective measures, and instructional techniques regarding coping mechanisms utilized by EFL teachers, whereas multiple factors encounter difficulties when teaching low achievers.
Text simplification as a controlled text style transfer task

The task of text simplification is to reduce the complexity of the given piece of text while preserving its original meaning to improve readability and understanding. In this paper, we consider the simplification task as a subfield of the general text style transfer problem and apply methods of controllable text style to rewrite texts in a simpler manner preserving their meaning. Namely, we use a paraphrase model guided by another style-conditional language model. In our work, we perform a series of experiments and compare this approach with the standard fine-tuning of an autoregressive model.
Estimating cognitive text complexity with aggregation of quantile-based models

In this paper, we introduce a novel approach to estimating the cognitive complexity of a text at different levels of language: phonetic, morphemic, lexical, and syntactic. The proposed method detects tokens with an abnormal frequency of complexity scores. The frequencies are taken from the empirical distributions calculated over the reference corpus of texts. We use the Russian Wikipedia for this purpose. Ensemble models are combined from individual models from different language levels. We created datasets of pairs of text fragments taken from social studies textbooks of different grades to train the ensembles. Empirical evidence shows that the proposed approach outperforms existing methods, such as readability indices, in estimating text complexity in terms of accuracy. The purpose of this study is to create one of the important components of the system of recommendation of scientific and educational content.
An attempt to determine a preposition and delimit the class of derived prepositions in Russian

The object of the paper are Russian words traditionally described as derived prepositions. The problem is that there is no formal definition of preposition in theoretical or applied linguistics. Non-derivative, or primitive prepositions are given in grammar by the closed list, so strictly speaking there is no need to define this class of words. However. we must have criteria for determining derived prepositions. I suggest a set of necessary conditions that a preposition must satisfy. I demonstrate that so called adverbial prepositionsin Russian do not satisfy them and should be described as adverbs. Similarly, some Russian verbal prepositions, and some Russian denominative prepositions should not be described as prepositions.
Text VQA with Token Classification of Recognized Text and Rule-Based Numerical Reasoning

In this paper, we describe a question answering system on document images which is capable of numerical reasoning over extracted structured data. The system performs optical character recognition, detection of key attributes in text, generation of a numerical reasoning program, and its execution with the values of key attributes as operands. OCR includes the steps of bounding boxes detection and recognition of text from bounding boxes. The extraction of key attributes, such as quantity and price of goods, total etc., is based on the BERT token classification model. For expression generation we investigated the rule-based approach and the T5-base model and found that T5 is capable of generalization to expression types unseen in the training set. The proposed architecture of the question answering system utilizes the structure of independent blocks, each of which can be enhanced or replaced while keeping other components unchanged. The proposed model was evaluated in the Receipt-AVQA competition and on FUNSD dataset.
Enhancing the semantic and conceptual description of Ancient Greek verbs in WordNet with VerbNet and FrameNet: a treebank-based study

This paper presents a pilot study intended to enhance the semantic and conceptual description of Ancient Greek verbs in WordNet with information from two other resources, VerbNet and FrameNet, and to enrich a treebank of Ancient Greek texts with semantic information extracted from the three resources. We provided semantic annotation for verbs based on their morphosyntactic behavior, and performed a number of queries in order to extract occurrences from the Ancient Greek treebank that intended to match the different meanings of each verb. The manual check of the data extracted shows that, in spite of a limited number of mismatches, our queries yielded reliable results. The queries can be further refined in the future and complemented with a rule-based algorithm to map frame elements to dependency structure.
The role of Indicators in Argumentative Relation Prediction

The article presents a comparative study of methods for argumentative relation prediction based on a neural network approach. The distinctive feature of the study is the use of argumentative indicators in the preparation of the training sample. The indicators are generated based on the discourse marker dictionary. The experiments were carried out using an annotated corpus of scientific and popular science texts, including 162 articles available on the ArgNetBank Studio web platform. A set of all argumentative relations is described by internal connections of arguments and include the conclusion and the premise. In the first stage of training set construction, fragments of text that included two consecutive sentences were examined. In the second stage, indicators were retrieved from the corpus texts and, for each indicator, statements presumably corresponding to the premise and conclusion of the argument were extracted. In total, 4.2 thousand indicator-based training contexts and 13.6 thousand pairs of sentences were obtained from the corpus with annotation of the presence of an argumentative relation. Based on this training sample, four classifiers were built: without indicators, with marking indicators in sentences using tags, taking into account segmentation of text based on indicators, with segmentation and tags. The results of the experiments on argumentative relation prediction are presented.
Modified PSNR Metric for Watermarked-Image Assessment

Digital images are exposed to different types of noise due to transformation, compression, or external data embedding. The assessment of these images is important to check their validity before being used in different applications. Because of its simplicity and straightforward calculations, the Peak Signal to Noise Ratio (PSNR) is used for evaluating digital images. PSNR, However, does not account for the human eye bias in preserving noise between textured and smooth areas. In this paper, a modified PSNR, named texture-based PSNR (T-PSNR), is proposed to enhance the PSNR metric for better image quality assessment. The metric is designed to be applied for watermarked image evaluation.
FORMATION OF INFORMATION AND COMMUNICATION READINESS OF TEACHERS OF PRESCHOOL EDUCATION IN THE CONDITIONS OF CONTINUING PROFESSIONAL EDUCATION

The article is devoted to the formation of information and communication readiness of preschool teachers in the conditions of continuing professional education in Kyrgyzstan. The main problems and challenges faced by teachers in this field are highlighted, including insufficient training programs and materials, limited access to technical means and the Internet, as well as poor quality and availability of educational resources. The methods of teaching teachers the basics of ICT, specialized training on the use of ICT in the educational process, as well as the importance of teaching digital literacy and security are discussed. In conclusion, the need for further research and development of strategies and techniques to overcome the problems associated with the formation of information and communication readiness of preschool teachers in the conditions of continuing professional education in Kyrgyzstan is emphasized.
About Cloud Request Protection

The article examines the well-known cryptographic problem of obtaining data from a database by a client so that no one with access to the server except the client himself could obtain information about this request. This problem known as PIR (Private Information Retrieval) was formulated in 1995 by Chor, Goldreich, Kushilevitz and Sudan in the information-theoretic setting. A model of cloud computing is proposed. It includes a cloud, an authentication center, a user, clients, trusted dealer, an active adversary executing the protocol in the cloud. The attacking side has the ability to create fake clients to generate an unlimited number of requests. An algorithm for the organization and database distribution on the cloud and an algorithm for obtaining the required bit were proposed. An injective transformation of bit numbers represented in the l-ary number system by words of length d into words without repeating digits of the same length with an alphabet of 𝒍̂ digits is used, i.e. a transformation {𝟎, ..., 𝒍 − 𝟏}^𝒅 → {𝟎, ..., 𝒍̂ − 𝟏}^𝒅 was constructed. This transformation reduces the probability of disclosure of the requested bit number. The communication complexity and probability of revealing required bit were estimated, taking into account the performed transformation.